π The many facets of community detection in complex networks
Michael T. Schaub, Jean-Charles Delvenne, Martin Rosvall, Renaud Lambiotte
https://arxiv.org/pdf/1611.07769v1
π ABSTRACT
Community detection, the decomposition of a graph into meaningful building blocks, has been a core research topic in network science over the past years. Since a precise notion of what constitutes a community has remained evasive, community detection algorithms have often been compared on benchmark graphs with a particular form of community structure, and classified based on the mathematical techniques they employ. However, this can be misleading because apparent similarities in their mathematical machinery can disguise entirely different objectives. Here we provide a focused review of the different motivations that underpin community detection. This problem-driven classification is useful in applied network science, where it is important to select an appropriate algorithm for the given purpose. Moreover, highlighting the different facets of community detection also delineates the many lines of research, and points out open directions and avenues for future research.
#Social and #Information #Networks (cs.SI); #Data_Analysis, #Statistics and #Probability (physics.data-an); #Physics and #Society (physics.soc-ph
Michael T. Schaub, Jean-Charles Delvenne, Martin Rosvall, Renaud Lambiotte
https://arxiv.org/pdf/1611.07769v1
π ABSTRACT
Community detection, the decomposition of a graph into meaningful building blocks, has been a core research topic in network science over the past years. Since a precise notion of what constitutes a community has remained evasive, community detection algorithms have often been compared on benchmark graphs with a particular form of community structure, and classified based on the mathematical techniques they employ. However, this can be misleading because apparent similarities in their mathematical machinery can disguise entirely different objectives. Here we provide a focused review of the different motivations that underpin community detection. This problem-driven classification is useful in applied network science, where it is important to select an appropriate algorithm for the given purpose. Moreover, highlighting the different facets of community detection also delineates the many lines of research, and points out open directions and avenues for future research.
#Social and #Information #Networks (cs.SI); #Data_Analysis, #Statistics and #Probability (physics.data-an); #Physics and #Society (physics.soc-ph
π Universality of the SIS prevalence in networks
Piet Van Mieghem
https://arxiv.org/pdf/1612.01386v1
π ABSTRACT
Epidemic models are increasingly used in real-world networks to understand diffusion phenomena (such as the spread of diseases, emotions, innovations, failures) or the transport of information (such as news, memes in social on-line networks). A new analysis of the prevalence, the expected number of infected nodes in a network, is presented and physically interpreted. The analysis method is based on spectral decomposition and leads to a universal, analytic curve, that can bound the time-varying prevalence in any finite time interval. Moreover, that universal curve also applies to various types of Susceptible-Infected-Susceptible (SIS) (and Susceptible-Infected-Removed (SIR)) infection processes, with both homogenous and heterogeneous infection characteristics (curing and infection rates), in temporal and even disconnected graphs and in SIS processes with and without self-infections. The accuracy of the universal curve is comparable to that of well-established mean-field approximations.
Subjects: #Physics and #Society (physics.soc-ph); #Social and #Information #Networks (cs.SI); #Populations and #Evolution (q-bio.PE)
Piet Van Mieghem
https://arxiv.org/pdf/1612.01386v1
π ABSTRACT
Epidemic models are increasingly used in real-world networks to understand diffusion phenomena (such as the spread of diseases, emotions, innovations, failures) or the transport of information (such as news, memes in social on-line networks). A new analysis of the prevalence, the expected number of infected nodes in a network, is presented and physically interpreted. The analysis method is based on spectral decomposition and leads to a universal, analytic curve, that can bound the time-varying prevalence in any finite time interval. Moreover, that universal curve also applies to various types of Susceptible-Infected-Susceptible (SIS) (and Susceptible-Infected-Removed (SIR)) infection processes, with both homogenous and heterogeneous infection characteristics (curing and infection rates), in temporal and even disconnected graphs and in SIS processes with and without self-infections. The accuracy of the universal curve is comparable to that of well-established mean-field approximations.
Subjects: #Physics and #Society (physics.soc-ph); #Social and #Information #Networks (cs.SI); #Populations and #Evolution (q-bio.PE)
π Applied Social Network Analysis in Python
https://www.coursera.org/learn/python-social-network-analysis
π About this course:
This course will introduce the learner to network modelling through the #networkx toolset. Used to model knowledge graphs and physical and virtual #networks, the lens will be social network analysis. The course begins with an understanding of what network modelling is (#graph_theory) and motivations for why we might model phenomena as networks. The second week introduces the networkx library and discusses how to build and #visualize networks. The third week will describe #metrics as they relate to the networks and demonstrate how these metrics can be applied to graph structures. The final week will explore the #social networking analysis workflow, from problem identification through to generation of insight.
https://www.coursera.org/learn/python-social-network-analysis
π About this course:
This course will introduce the learner to network modelling through the #networkx toolset. Used to model knowledge graphs and physical and virtual #networks, the lens will be social network analysis. The course begins with an understanding of what network modelling is (#graph_theory) and motivations for why we might model phenomena as networks. The second week introduces the networkx library and discusses how to build and #visualize networks. The third week will describe #metrics as they relate to the networks and demonstrate how these metrics can be applied to graph structures. The final week will explore the #social networking analysis workflow, from problem identification through to generation of insight.
π Multichannel social signatures and persistent features of ego networks
Sara Heydari, Sam G. Roberts, Robin I. M. Dunbar, and Jari SaramΓ€ki
π https://rdcu.be/QmwK
π ABSTRACT
The structure of egocentric networks reflects the way people balance their need for strong, emotionally intense relationships and a diversity of weaker ties. Egocentric network structure can be quantified with βsocial signaturesβ, which describe how people distribute their communication effort across the members (alters) of their personal networks. Social signatures based on call data have indicated that people mostly communicate with a few close alters; they also have persistent, distinct signatures. To examine if these results hold for other channels of communication, here we compare social signatures built from call and text message data, and develop a way of constructing mixed social signatures using both channels. We observe that all types of signatures display persistent individual differences that remain stable despite the turnover in individual alters. We also show that call, text, and mixed signatures resemble one another both at the population level and at the level of individuals. The consistency of social signatures across individuals for different channels of communication is surprising because the choice of channel appears to be alter-specific with no clear overall pattern, and ego networks constructed from calls and texts overlap only partially in terms of alters. These results demonstrate individuals vary in how they allocate their communication effort across their personal networks and this variation is persistent over time and across different channels of communication.
#Social_signatures, #Social_networks, #Egocentric_networks, #Mobile_phones
Sara Heydari, Sam G. Roberts, Robin I. M. Dunbar, and Jari SaramΓ€ki
π https://rdcu.be/QmwK
π ABSTRACT
The structure of egocentric networks reflects the way people balance their need for strong, emotionally intense relationships and a diversity of weaker ties. Egocentric network structure can be quantified with βsocial signaturesβ, which describe how people distribute their communication effort across the members (alters) of their personal networks. Social signatures based on call data have indicated that people mostly communicate with a few close alters; they also have persistent, distinct signatures. To examine if these results hold for other channels of communication, here we compare social signatures built from call and text message data, and develop a way of constructing mixed social signatures using both channels. We observe that all types of signatures display persistent individual differences that remain stable despite the turnover in individual alters. We also show that call, text, and mixed signatures resemble one another both at the population level and at the level of individuals. The consistency of social signatures across individuals for different channels of communication is surprising because the choice of channel appears to be alter-specific with no clear overall pattern, and ego networks constructed from calls and texts overlap only partially in terms of alters. These results demonstrate individuals vary in how they allocate their communication effort across their personal networks and this variation is persistent over time and across different channels of communication.
#Social_signatures, #Social_networks, #Egocentric_networks, #Mobile_phones
π° Three new #PhD positions in my lab! Broadly focussing on #networks, #dynamics, and #data analysis in #biodiversity and #social systems. (more details soon)
https://t.co/ogcitWzgOk
https://t.co/ogcitWzgOk
The new Master's programme Computational Social Systems is largely influenced by David Garcia's (@dgarcia_eu) work. More about him and further links to the programme: https://t.co/tKXIzD7NZh
#study #master #programme #computational #social #science
#study #master #programme #computational #social #science
www.tugraz.at
Computational Social Sciences: Interplay Between Computer and Human
How emotional are we online? Is social media fuelling the polarization of our society? Can I inquire about the character of an artificial intelligence? David Garcia asks himself questions like these at TU Graz.
Long review article: "#Social_physics"
This is a collection of writings in the border region between physics and the social sciences stitched together by a heroic effort of Marko Jusup.
The world of social phenomena as seen through the eyes of physicists (especially statistical physicists). Lots of interesting ideas, history, applications, and open problems here.
(by Marko Jusup, Petter Holme, Kiyoshi Kanazawa, Misako Takayasu, Ivan Romic, Zhen Wang, Suncana Gecek, Tomislav Lipic, Boris Podobnik, Lin Wang, Wei Luo, Tin Klanjscek, Jingfang Fan, Stefano Boccaletti, Matjaz Perc):
https://t.co/j1hs4x8hyH
This is a collection of writings in the border region between physics and the social sciences stitched together by a heroic effort of Marko Jusup.
The world of social phenomena as seen through the eyes of physicists (especially statistical physicists). Lots of interesting ideas, history, applications, and open problems here.
(by Marko Jusup, Petter Holme, Kiyoshi Kanazawa, Misako Takayasu, Ivan Romic, Zhen Wang, Suncana Gecek, Tomislav Lipic, Boris Podobnik, Lin Wang, Wei Luo, Tin Klanjscek, Jingfang Fan, Stefano Boccaletti, Matjaz Perc):
https://t.co/j1hs4x8hyH
π¦ π Seriously, how much vaccination is enough to reach herd immunity? How does the expected epidemic size depend on homophily in vaccine adoption?
π arxiv.org/abs/2112.07538.
1) We find that already a small level of homophily (h) can considerably increase the critical vaccine coverage (ΟαΆα΅₯) required for herd immunity and that stronger homophily can push this threshold entirely out of reach.
2) Our framework is general enough to account for homophily by adherence to other epidemic interventions that reduce the susceptibility or infectiousness of individuals, such as #social_distancing practices, use of protective equipment, and adoption of digital #contact_tracing.
3) Vaccines' efficacy against susceptibility or transmitting the infection can vary. For perfect vacc, the epidemic size increases monotonically as a function of homophily, while for vacc with limited efficacy, the epidemic size is max. at an intermediate level of homophily.
π arxiv.org/abs/2112.07538.
1) We find that already a small level of homophily (h) can considerably increase the critical vaccine coverage (ΟαΆα΅₯) required for herd immunity and that stronger homophily can push this threshold entirely out of reach.
2) Our framework is general enough to account for homophily by adherence to other epidemic interventions that reduce the susceptibility or infectiousness of individuals, such as #social_distancing practices, use of protective equipment, and adoption of digital #contact_tracing.
3) Vaccines' efficacy against susceptibility or transmitting the infection can vary. For perfect vacc, the epidemic size increases monotonically as a function of homophily, while for vacc with limited efficacy, the epidemic size is max. at an intermediate level of homophily.
πTomorrow 6 May 3PM CEST webinar "#Modeling the #Network of #Social Interactions"
@janos_kertesz Prof at the Dep of Network & #DataScience Central European University and member of the ECLT Academic Assembly
πMore info https://t.co/7cStmEBLUA
@janos_kertesz Prof at the Dep of Network & #DataScience Central European University and member of the ECLT Academic Assembly
πMore info https://t.co/7cStmEBLUA