際際滷shows by User: petterholme / http://www.slideshare.net/images/logo.gif 際際滷shows by User: petterholme / Mon, 12 Jul 2021 14:44:27 GMT 際際滷Share feed for 際際滷shows by User: petterholme Temporal network epidemiology: Subtleties and algorithms /slideshow/temporal-network-epidemiology-subtleties-and-algorithms/249703417 durham-210712144427
The SIR and SIS models are the canonical model of epidemics of infections that make people immune upon recovery. Many open questions in computational epidemiology concern the underlying contact structures impact on models like the SIR or SIS. Temporal networks constitute a theoretical framework capable of encoding structures both in the networks of who could infect whom and when these contacts happen. In this talk, we discuss the detailed assumptions behind such simulationshow to make them comparable with analytically tractable formulations of the SIR model, and at the same time, as realistic as possible. We also discuss fast algorithms for such simulations and the challenges in improving them.]]>
The SIR and SIS models are the canonical model of epidemics of infections that make people immune upon recovery. Many open questions in computational epidemiology concern the underlying contact structures impact on models like the SIR or SIS. Temporal networks constitute a theoretical framework capable of encoding structures both in the networks of who could infect whom and when these contacts happen. In this talk, we discuss the detailed assumptions behind such simulationshow to make them comparable with analytically tractable formulations of the SIR model, and at the same time, as realistic as possible. We also discuss fast algorithms for such simulations and the challenges in improving them.]]> Mon, 12 Jul 2021 14:44:27 GMT /slideshow/temporal-network-epidemiology-subtleties-and-algorithms/249703417 petterholme@slideshare.net(petterholme) Temporal network epidemiology: Subtleties and algorithms petterholme The SIR and SIS models are the canonical model of epidemics of infections that make people immune upon recovery. Many open questions in computational epidemiology concern the underlying contact structures impact on models like the SIR or SIS. Temporal networks constitute a theoretical framework capable of encoding structures both in the networks of who could infect whom and when these contacts happen. In this talk, we discuss the detailed assumptions behind such simulationshow to make them comparable with analytically tractable formulations of the SIR model, and at the same time, as realistic as possible. We also discuss fast algorithms for such simulations and the challenges in improving them. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/durham-210712144427-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The SIR and SIS models are the canonical model of epidemics of infections that make people immune upon recovery. Many open questions in computational epidemiology concern the underlying contact structures impact on models like the SIR or SIS. Temporal networks constitute a theoretical framework capable of encoding structures both in the networks of who could infect whom and when these contacts happen. In this talk, we discuss the detailed assumptions behind such simulationshow to make them comparable with analytically tractable formulations of the SIR model, and at the same time, as realistic as possible. We also discuss fast algorithms for such simulations and the challenges in improving them.
Temporal network epidemiology: Subtleties and algorithms from Petter Holme
]]> 294 0 https://cdn.slidesharecdn.com/ss_thumbnails/durham-210712144427-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 The big science of small networks /slideshow/the-big-science-of-small-networks/222277917 netscix2020-200121021400
7 arguments why you should start studying small networks]]>
7 arguments why you should start studying small networks]]> Tue, 21 Jan 2020 02:14:00 GMT /slideshow/the-big-science-of-small-networks/222277917 petterholme@slideshare.net(petterholme) The big science of small networks petterholme 7 arguments why you should start studying small networks <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/netscix2020-200121021400-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> 7 arguments why you should start studying small networks
The big science of small networks from Petter Holme
]]> 560 4 https://cdn.slidesharecdn.com/ss_thumbnails/netscix2020-200121021400-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Spin models on networks revisited /slideshow/spin-models-on-networks-revisited/178735445 ccs2019-191003065441
Presented at the Criticality in Socioeconomic Systems at CCS 2019]]>
Presented at the Criticality in Socioeconomic Systems at CCS 2019]]> Thu, 03 Oct 2019 06:54:41 GMT /slideshow/spin-models-on-networks-revisited/178735445 petterholme@slideshare.net(petterholme) Spin models on networks revisited petterholme Presented at the Criticality in Socioeconomic Systems at CCS 2019 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ccs2019-191003065441-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Presented at the Criticality in Socioeconomic Systems at CCS 2019
Spin models on networks revisited from Petter Holme
]]> 588 0 https://cdn.slidesharecdn.com/ss_thumbnails/ccs2019-191003065441-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 History of social simulations /slideshow/history-of-social-simulations/120927372 kobeother-181027091751
This is one segment of a talk where I presented the history of computational social science: * The origins of computer simulations. * The trouble to publish computational studies in the 1960s. * The peak enthusiasm for computer simulations after "Limits of Growth" * The precursors of social-media data science in the 1980's]]>
This is one segment of a talk where I presented the history of computational social science: * The origins of computer simulations. * The trouble to publish computational studies in the 1960s. * The peak enthusiasm for computer simulations after "Limits of Growth" * The precursors of social-media data science in the 1980's]]> Sat, 27 Oct 2018 09:17:51 GMT /slideshow/history-of-social-simulations/120927372 petterholme@slideshare.net(petterholme) History of social simulations petterholme This is one segment of a talk where I presented the history of computational social science: * The origins of computer simulations. * The trouble to publish computational studies in the 1960s. * The peak enthusiasm for computer simulations after "Limits of Growth" * The precursors of social-media data science in the 1980's <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/kobeother-181027091751-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This is one segment of a talk where I presented the history of computational social science: * The origins of computer simulations. * The trouble to publish computational studies in the 1960s. * The peak enthusiasm for computer simulations after &quot;Limits of Growth&quot; * The precursors of social-media data science in the 1980&#39;s
History of social simulations from Petter Holme
]]> 267 2 https://cdn.slidesharecdn.com/ss_thumbnails/kobeother-181027091751-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Optimizing sentinel surveillance in static and temporal networks /slideshow/optimizing-sentinel-surveillance-in-static-and-temporal-networks/101888642 sentinel-180611093418
talk at the Contagion & Networks Satellite Sympoisum, NetSci 2018]]>
talk at the Contagion & Networks Satellite Sympoisum, NetSci 2018]]> Mon, 11 Jun 2018 09:34:18 GMT /slideshow/optimizing-sentinel-surveillance-in-static-and-temporal-networks/101888642 petterholme@slideshare.net(petterholme) Optimizing sentinel surveillance in static and temporal networks petterholme talk at the Contagion & Networks Satellite Sympoisum, NetSci 2018 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sentinel-180611093418-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> talk at the Contagion &amp; Networks Satellite Sympoisum, NetSci 2018
Optimizing sentinel surveillance in static and temporal networks from Petter Holme
]]> 818 5 https://cdn.slidesharecdn.com/ss_thumbnails/sentinel-180611093418-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Important spreaders in networks: Exact results for small graphs /slideshow/important-spreaders-in-networks-exact-results-for-small-graphs/86134576 siretc-180114153504
Presentation at NetSci-X Jan 7, 2018]]>
Presentation at NetSci-X Jan 7, 2018]]> Sun, 14 Jan 2018 15:35:04 GMT /slideshow/important-spreaders-in-networks-exact-results-for-small-graphs/86134576 petterholme@slideshare.net(petterholme) Important spreaders in networks: Exact results for small graphs petterholme Presentation at NetSci-X Jan 7, 2018 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/siretc-180114153504-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Presentation at NetSci-X Jan 7, 2018
Important spreaders in networks: Exact results for small graphs from Petter Holme
]]> 534 2 https://cdn.slidesharecdn.com/ss_thumbnails/siretc-180114153504-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Important spreaders in networks: exact results on small graphs /slideshow/important-spreaders-in-networks-exact-results-on-small-graphs/80920423 myworkonsiretc-171018000948
To be able to control spreading phenomena (like the spreading of diseases and information) in networks it is important to identify influential spreaders. What "important" means depends on what is spreading and what kind of countermeasures that are available. In this work, we let the susceptible-infected-removed (SIR) model represent the spreading dynamics and contrast three different definitions of importance: Influence maximization (the expected outbreak size given a set of seed nodes), the effect of vaccination (how much deleting nodes would reduce the expected outbreak size) and sentinel surveillance (how early an outbreak could be detected with sensors at a set of nodes). We calculate the exact expressions of these quantities, as functions of the SIR parameters, for all connected graphs of three to seven nodes. We obtain the smallest graphs where the optimal node sets are not overlapping. We find that: node separation is more important than centrality for more than one active node, that vaccination and influence maximization are the most different aspects of importance, and that the three aspects are more similar when the infection rate is low. Furthermore, we discuss similar approaches to study the extinction times in the susceptible-infected- susceptible model.]]>
To be able to control spreading phenomena (like the spreading of diseases and information) in networks it is important to identify influential spreaders. What "important" means depends on what is spreading and what kind of countermeasures that are available. In this work, we let the susceptible-infected-removed (SIR) model represent the spreading dynamics and contrast three different definitions of importance: Influence maximization (the expected outbreak size given a set of seed nodes), the effect of vaccination (how much deleting nodes would reduce the expected outbreak size) and sentinel surveillance (how early an outbreak could be detected with sensors at a set of nodes). We calculate the exact expressions of these quantities, as functions of the SIR parameters, for all connected graphs of three to seven nodes. We obtain the smallest graphs where the optimal node sets are not overlapping. We find that: node separation is more important than centrality for more than one active node, that vaccination and influence maximization are the most different aspects of importance, and that the three aspects are more similar when the infection rate is low. Furthermore, we discuss similar approaches to study the extinction times in the susceptible-infected- susceptible model.]]> Wed, 18 Oct 2017 00:09:48 GMT /slideshow/important-spreaders-in-networks-exact-results-on-small-graphs/80920423 petterholme@slideshare.net(petterholme) Important spreaders in networks: exact results on small graphs petterholme To be able to control spreading phenomena (like the spreading of diseases and information) in networks it is important to identify influential spreaders. What "important" means depends on what is spreading and what kind of countermeasures that are available. In this work, we let the susceptible-infected-removed (SIR) model represent the spreading dynamics and contrast three different definitions of importance: Influence maximization (the expected outbreak size given a set of seed nodes), the effect of vaccination (how much deleting nodes would reduce the expected outbreak size) and sentinel surveillance (how early an outbreak could be detected with sensors at a set of nodes). We calculate the exact expressions of these quantities, as functions of the SIR parameters, for all connected graphs of three to seven nodes. We obtain the smallest graphs where the optimal node sets are not overlapping. We find that: node separation is more important than centrality for more than one active node, that vaccination and influence maximization are the most different aspects of importance, and that the three aspects are more similar when the infection rate is low. Furthermore, we discuss similar approaches to study the extinction times in the susceptible-infected- susceptible model. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/myworkonsiretc-171018000948-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> To be able to control spreading phenomena (like the spreading of diseases and information) in networks it is important to identify influential spreaders. What &quot;important&quot; means depends on what is spreading and what kind of countermeasures that are available. In this work, we let the susceptible-infected-removed (SIR) model represent the spreading dynamics and contrast three different definitions of importance: Influence maximization (the expected outbreak size given a set of seed nodes), the effect of vaccination (how much deleting nodes would reduce the expected outbreak size) and sentinel surveillance (how early an outbreak could be detected with sensors at a set of nodes). We calculate the exact expressions of these quantities, as functions of the SIR parameters, for all connected graphs of three to seven nodes. We obtain the smallest graphs where the optimal node sets are not overlapping. We find that: node separation is more important than centrality for more than one active node, that vaccination and influence maximization are the most different aspects of importance, and that the three aspects are more similar when the infection rate is low. Furthermore, we discuss similar approaches to study the extinction times in the susceptible-infected- susceptible model.
Important spreaders in networks: exact results on small graphs from Petter Holme
]]> 680 2 https://cdn.slidesharecdn.com/ss_thumbnails/myworkonsiretc-171018000948-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Netsci 2017 /petterholme/netsci-2017 netsci2017-170619200724
Morphology of travel routes and the organization of cities]]>
Morphology of travel routes and the organization of cities]]> Mon, 19 Jun 2017 20:07:24 GMT /petterholme/netsci-2017 petterholme@slideshare.net(petterholme) Netsci 2017 petterholme Morphology of travel routes and the organization of cities <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/netsci2017-170619200724-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Morphology of travel routes and the organization of cities
Netsci 2017 from Petter Holme
]]> 776 2 https://cdn.slidesharecdn.com/ss_thumbnails/netsci2017-170619200724-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Spreading processes on temporal networks /slideshow/spreading-processes-on-temporal-networks/61462387 tmpnwk-160428131248
My talk at the workshop Critical and collective effects in graphs and networks, April 28, 2016. http://discrete-mathematics.org/?page_id=451]]>
My talk at the workshop Critical and collective effects in graphs and networks, April 28, 2016. http://discrete-mathematics.org/?page_id=451]]> Thu, 28 Apr 2016 13:12:47 GMT /slideshow/spreading-processes-on-temporal-networks/61462387 petterholme@slideshare.net(petterholme) Spreading processes on temporal networks petterholme My talk at the workshop Critical and collective effects in graphs and networks, April 28, 2016. http://discrete-mathematics.org/?page_id=451 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/tmpnwk-160428131248-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> My talk at the workshop Critical and collective effects in graphs and networks, April 28, 2016. http://discrete-mathematics.org/?page_id=451
Spreading processes on temporal networks from Petter Holme
]]> 996 7 https://cdn.slidesharecdn.com/ss_thumbnails/tmpnwk-160428131248-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Dynamics of Internet-mediated partnership formation /slideshow/dynamics-of-internetmediated-partnership-formation/53049550 escorts-150922071839-lva1-app6891
A talk summarizing works on Internet dating and prostitution by me, Fredrik Liljeros and Luis Rocha.]]>
A talk summarizing works on Internet dating and prostitution by me, Fredrik Liljeros and Luis Rocha.]]> Tue, 22 Sep 2015 07:18:39 GMT /slideshow/dynamics-of-internetmediated-partnership-formation/53049550 petterholme@slideshare.net(petterholme) Dynamics of Internet-mediated partnership formation petterholme A talk summarizing works on Internet dating and prostitution by me, Fredrik Liljeros and Luis Rocha. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/escorts-150922071839-lva1-app6891-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> A talk summarizing works on Internet dating and prostitution by me, Fredrik Liljeros and Luis Rocha.
Dynamics of Internet-mediated partnership formation from Petter Holme
]]> 540 6 https://cdn.slidesharecdn.com/ss_thumbnails/escorts-150922071839-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Disease spreading & control in temporal networks /slideshow/disease-spreading-control-in-temporal-networks/53049055 kaist2010-150922070027-lva1-app6891
A presentation about this paper: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0036439]]>
A presentation about this paper: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0036439]]> Tue, 22 Sep 2015 07:00:26 GMT /slideshow/disease-spreading-control-in-temporal-networks/53049055 petterholme@slideshare.net(petterholme) Disease spreading & control in temporal networks petterholme A presentation about this paper: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0036439 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/kaist2010-150922070027-lva1-app6891-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> A presentation about this paper: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0036439
Disease spreading & control in temporal networks from Petter Holme
]]> 590 4 https://cdn.slidesharecdn.com/ss_thumbnails/kaist2010-150922070027-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Modeling the evolution of the AS-level Internet: Integrating aspects of traffic, geography and economy /slideshow/modeling-the-evolution-of-the-aslevel-internet-integrating-aspects-of-traffic-geography-and-economy/53048972 berlin-150922065625-lva1-app6891
Presentation of this paper: http://dl.acm.org/citation.cfm?id=1384611]]>
Presentation of this paper: http://dl.acm.org/citation.cfm?id=1384611]]> Tue, 22 Sep 2015 06:56:25 GMT /slideshow/modeling-the-evolution-of-the-aslevel-internet-integrating-aspects-of-traffic-geography-and-economy/53048972 petterholme@slideshare.net(petterholme) Modeling the evolution of the AS-level Internet: Integrating aspects of traffic, geography and economy petterholme Presentation of this paper: http://dl.acm.org/citation.cfm?id=1384611 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/berlin-150922065625-lva1-app6891-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Presentation of this paper: http://dl.acm.org/citation.cfm?id=1384611
Modeling the evolution of the AS-level Internet: Integrating aspects of traffic, geography and economy from Petter Holme
]]> 365 4 https://cdn.slidesharecdn.com/ss_thumbnails/berlin-150922065625-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Emergence of collective memories /slideshow/emergence-of-collective-memories/53048827 memoryweb-150922065054-lva1-app6892
Presentation of this paper: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0012522]]>
Presentation of this paper: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0012522]]> Tue, 22 Sep 2015 06:50:54 GMT /slideshow/emergence-of-collective-memories/53048827 petterholme@slideshare.net(petterholme) Emergence of collective memories petterholme Presentation of this paper: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0012522 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/memoryweb-150922065054-lva1-app6892-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Presentation of this paper: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0012522
Emergence of collective memories from Petter Holme
]]> 689 6 https://cdn.slidesharecdn.com/ss_thumbnails/memoryweb-150922065054-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 A paradox of importance in network epidemiology /slideshow/a-paradox-of-importance-in-network-epidemiology-49163224/49163224 is2c2print-150609093409-lva1-app6892
Talk at the International Conference on Computational Social Science, Helsinki, June 9, 2015. On YouTube here (Plenary II): https://www.youtube.com/channel/UCUGsbLwL4G2CQQfk95oZjVw]]>
Talk at the International Conference on Computational Social Science, Helsinki, June 9, 2015. On YouTube here (Plenary II): https://www.youtube.com/channel/UCUGsbLwL4G2CQQfk95oZjVw]]> Tue, 09 Jun 2015 09:34:09 GMT /slideshow/a-paradox-of-importance-in-network-epidemiology-49163224/49163224 petterholme@slideshare.net(petterholme) A paradox of importance in network epidemiology petterholme Talk at the International Conference on Computational Social Science, Helsinki, June 9, 2015. On YouTube here (Plenary II): https://www.youtube.com/channel/UCUGsbLwL4G2CQQfk95oZjVw <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/is2c2print-150609093409-lva1-app6892-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Talk at the International Conference on Computational Social Science, Helsinki, June 9, 2015. On YouTube here (Plenary II): https://www.youtube.com/channel/UCUGsbLwL4G2CQQfk95oZjVw
A paradox of importance in network epidemiology from Petter Holme
]]> 2138 3 https://cdn.slidesharecdn.com/ss_thumbnails/is2c2print-150609093409-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 How the information content of your contact pattern representation affects predictability of epidemics /slideshow/how-the-information-content-of-your-contact-pattern-representation-affects-predictability-of-epidemics/48880251 sirpred-150602090321-lva1-app6892
Presentation at the HONS satellite of NetSci 2015, Zaragoza, Spain]]>
Presentation at the HONS satellite of NetSci 2015, Zaragoza, Spain]]> Tue, 02 Jun 2015 09:03:21 GMT /slideshow/how-the-information-content-of-your-contact-pattern-representation-affects-predictability-of-epidemics/48880251 petterholme@slideshare.net(petterholme) How the information content of your contact pattern representation affects predictability of epidemics petterholme Presentation at the HONS satellite of NetSci 2015, Zaragoza, Spain <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sirpred-150602090321-lva1-app6892-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Presentation at the HONS satellite of NetSci 2015, Zaragoza, Spain
How the information content of your contact pattern representation affects predictability of epidemics from Petter Holme
]]> 1135 1 https://cdn.slidesharecdn.com/ss_thumbnails/sirpred-150602090321-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 From land use to human mobility /slideshow/from-land-use-to-human-mobility/48849702 urbannet-150601162233-lva1-app6891
Talk for the NetSci satellite UrbanNet, Zaragoza, Spain, June 1, 2015]]>
Talk for the NetSci satellite UrbanNet, Zaragoza, Spain, June 1, 2015]]> Mon, 01 Jun 2015 16:22:33 GMT /slideshow/from-land-use-to-human-mobility/48849702 petterholme@slideshare.net(petterholme) From land use to human mobility petterholme Talk for the NetSci satellite UrbanNet, Zaragoza, Spain, June 1, 2015 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/urbannet-150601162233-lva1-app6891-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Talk for the NetSci satellite UrbanNet, Zaragoza, Spain, June 1, 2015
From land use to human mobility from Petter Holme
]]> 1461 3 https://cdn.slidesharecdn.com/ss_thumbnails/urbannet-150601162233-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Why do metabolic networks look like they do? /petterholme/metabolic-48643199 metabolic-150527064534-lva1-app6891
Summarizing my research on metabolic networks.]]>
Summarizing my research on metabolic networks.]]> Wed, 27 May 2015 06:45:34 GMT /petterholme/metabolic-48643199 petterholme@slideshare.net(petterholme) Why do metabolic networks look like they do? petterholme Summarizing my research on metabolic networks. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/metabolic-150527064534-lva1-app6891-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Summarizing my research on metabolic networks.
Why do metabolic networks look like they do? from Petter Holme
]]> 711 3 https://cdn.slidesharecdn.com/ss_thumbnails/metabolic-150527064534-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Temporal Networks of Human Interaction /slideshow/temporal-networks-of-human-interaction/48027500 tnhi-150512031650-lva1-app6891
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]]> Tue, 12 May 2015 03:16:50 GMT /slideshow/temporal-networks-of-human-interaction/48027500 petterholme@slideshare.net(petterholme) Temporal Networks of Human Interaction petterholme <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/tnhi-150512031650-lva1-app6891-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br>
Temporal Networks of Human Interaction from Petter Holme
]]> 3189 5 https://cdn.slidesharecdn.com/ss_thumbnails/tnhi-150512031650-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Modeling the fat tails of size fluctuations in organizations /slideshow/posco-43979141/43979141 posco-150128010115-conversion-gate02
Invited at Physics of Social Complexity (PoSCo), Pohang, Korea, January 28 2015. Presenting the paper by Mondani, Holme, Liljeros (2014) http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0100527]]>
Invited at Physics of Social Complexity (PoSCo), Pohang, Korea, January 28 2015. Presenting the paper by Mondani, Holme, Liljeros (2014) http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0100527]]> Wed, 28 Jan 2015 01:01:14 GMT /slideshow/posco-43979141/43979141 petterholme@slideshare.net(petterholme) Modeling the fat tails of size fluctuations in organizations petterholme Invited at Physics of Social Complexity (PoSCo), Pohang, Korea, January 28 2015. Presenting the paper by Mondani, Holme, Liljeros (2014) http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0100527 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/posco-150128010115-conversion-gate02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Invited at Physics of Social Complexity (PoSCo), Pohang, Korea, January 28 2015. Presenting the paper by Mondani, Holme, Liljeros (2014) http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0100527
Modeling the fat tails of size fluctuations in organizations from Petter Holme
]]> 661 3 https://cdn.slidesharecdn.com/ss_thumbnails/posco-150128010115-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 From temporal to static networks, and back /slideshow/from-temporal-to-static-networks-and-back/26826784 andbacktnets-131003090840-phpapp01
Infectious diseases are a major burden to global health. Understanding their mechanisms and being able to predict and intervene epidemic outbreaks is an important challenge for researchers and decision makers alike. It should not be too hard eitherif we include human contact patterns, the mechanisms of contagion and the typical features of the disease, we could model most infectious-disease related phenomena. Of these three components, the network epidemiology of the last decade has shown that our limited understanding of human contact patterns is probably the most important focus are for advancing infectious disease epidemiology. We will discuss what is known about human contact patterns and how to include this knowledge in epidemic modeling. First, we discuss recent work on what the epidemiologically most important temporal structures of human contacts are. We use about 80 empirical temporal network datasets, several arguably important for disease spreading, and scan the entire parameter space of disease-spreading models. By comparing to null-models, we identify important, simple temporal patterns that affect disease spreading stronger than the bursty interevent time distributions. Furthermore, we investigate how to eliminate the temporal information to make an as relevant static network as possible. After all, static network epidemiology has more methods and results than temporal network epidemiology and it for some purposes it is necessary. We find that an exponential threshold representation almost always the best performance, but time-sliced network (with a carefully chosen window, usually considerably different than the sampling time of the data) works almost as good. In contrast, networks of concurrent contacts do not seem to carry so important information.]]>
Infectious diseases are a major burden to global health. Understanding their mechanisms and being able to predict and intervene epidemic outbreaks is an important challenge for researchers and decision makers alike. It should not be too hard eitherif we include human contact patterns, the mechanisms of contagion and the typical features of the disease, we could model most infectious-disease related phenomena. Of these three components, the network epidemiology of the last decade has shown that our limited understanding of human contact patterns is probably the most important focus are for advancing infectious disease epidemiology. We will discuss what is known about human contact patterns and how to include this knowledge in epidemic modeling. First, we discuss recent work on what the epidemiologically most important temporal structures of human contacts are. We use about 80 empirical temporal network datasets, several arguably important for disease spreading, and scan the entire parameter space of disease-spreading models. By comparing to null-models, we identify important, simple temporal patterns that affect disease spreading stronger than the bursty interevent time distributions. Furthermore, we investigate how to eliminate the temporal information to make an as relevant static network as possible. After all, static network epidemiology has more methods and results than temporal network epidemiology and it for some purposes it is necessary. We find that an exponential threshold representation almost always the best performance, but time-sliced network (with a carefully chosen window, usually considerably different than the sampling time of the data) works almost as good. In contrast, networks of concurrent contacts do not seem to carry so important information.]]> Thu, 03 Oct 2013 09:08:40 GMT /slideshow/from-temporal-to-static-networks-and-back/26826784 petterholme@slideshare.net(petterholme) From temporal to static networks, and back petterholme Infectious diseases are a major burden to global health. Understanding their mechanisms and being able to predict and intervene epidemic outbreaks is an important challenge for researchers and decision makers alike. It should not be too hard eitherif we include human contact patterns, the mechanisms of contagion and the typical features of the disease, we could model most infectious-disease related phenomena. Of these three components, the network epidemiology of the last decade has shown that our limited understanding of human contact patterns is probably the most important focus are for advancing infectious disease epidemiology. We will discuss what is known about human contact patterns and how to include this knowledge in epidemic modeling. First, we discuss recent work on what the epidemiologically most important temporal structures of human contacts are. We use about 80 empirical temporal network datasets, several arguably important for disease spreading, and scan the entire parameter space of disease-spreading models. By comparing to null-models, we identify important, simple temporal patterns that affect disease spreading stronger than the bursty interevent time distributions. Furthermore, we investigate how to eliminate the temporal information to make an as relevant static network as possible. After all, static network epidemiology has more methods and results than temporal network epidemiology and it for some purposes it is necessary. We find that an exponential threshold representation almost always the best performance, but time-sliced network (with a carefully chosen window, usually considerably different than the sampling time of the data) works almost as good. In contrast, networks of concurrent contacts do not seem to carry so important information. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/andbacktnets-131003090840-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Infectious diseases are a major burden to global health. Understanding their mechanisms and being able to predict and intervene epidemic outbreaks is an important challenge for researchers and decision makers alike. It should not be too hard eitherif we include human contact patterns, the mechanisms of contagion and the typical features of the disease, we could model most infectious-disease related phenomena. Of these three components, the network epidemiology of the last decade has shown that our limited understanding of human contact patterns is probably the most important focus are for advancing infectious disease epidemiology. We will discuss what is known about human contact patterns and how to include this knowledge in epidemic modeling. First, we discuss recent work on what the epidemiologically most important temporal structures of human contacts are. We use about 80 empirical temporal network datasets, several arguably important for disease spreading, and scan the entire parameter space of disease-spreading models. By comparing to null-models, we identify important, simple temporal patterns that affect disease spreading stronger than the bursty interevent time distributions. Furthermore, we investigate how to eliminate the temporal information to make an as relevant static network as possible. After all, static network epidemiology has more methods and results than temporal network epidemiology and it for some purposes it is necessary. We find that an exponential threshold representation almost always the best performance, but time-sliced network (with a carefully chosen window, usually considerably different than the sampling time of the data) works almost as good. In contrast, networks of concurrent contacts do not seem to carry so important information.
From temporal to static networks, and back from Petter Holme
]]> 645 3 https://cdn.slidesharecdn.com/ss_thumbnails/andbacktnets-131003090840-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-petterholme-48x48.jpg?cb=1651234435 https://cdn.slidesharecdn.com/ss_thumbnails/durham-210712144427-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/temporal-network-epidemiology-subtleties-and-algorithms/249703417 Temporal network epide... https://cdn.slidesharecdn.com/ss_thumbnails/netscix2020-200121021400-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/the-big-science-of-small-networks/222277917 The big science of sma... https://cdn.slidesharecdn.com/ss_thumbnails/ccs2019-191003065441-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/spin-models-on-networks-revisited/178735445 Spin models on network...