Quantifying social group evolution

By Gergely Palla, Albert-László Barabási, Tamás Vicsek. Nature, 2007

1,698 citations recorded by the source corpus.

The rich set of interactions between individuals in society results in complex community structure, capturing highly connected circles of friends, families or professional cliques in a social network. Thanks to frequent changes in the activity and communication patterns of individuals, the associated social and communication network is subject to constant evolution. Our knowledge of the mechanisms governing the underlying community dynamics is limited, but is essential for a deeper understanding of the development and self-optimization of society as a whole. We have developed an algorithm based on clique percolation that allows us to investigate the time dependence of overlapping communities on a large scale, and thus uncover basic relationships characterizing community evolution. Our focus is on networks capturing the collaboration between scientists and the calls between mobile phone users. We find that large groups persist for longer if they are capable of dynamically altering their membership, suggesting that an ability to change the group composition results in better adaptability. The behaviour of small groups displays the opposite tendency—the condition for stability is that their composition remains unchanged. We also show that knowledge of the time commitment of members to a given community can be used for estimating the community’s lifetime. These findings offer insight into the fundamental differences between the dynamics of small groups and large institutions.

Subfields: Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/a0df4c9b5614417326d21f5d595f5a727e8b3a36

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Citations in the corpus

An analysis of parameter adaptation in reactive tabu search (2014, background): “…et al., 1999) with important applications in data mining (Boginski et al., 2006), computer vision (Pla and Marchant, 1997), social network analysis (Palla et al., 2007), computational bio-chemistry (Butenko and Wilhelm, 2005), bio-informatics (Ji et al., 2004; Mascia et al., 2010), genomics…”
Understanding the Spreading Patterns of Mobile Phone Viruses (2009, background): “Thus to quantitatively study the spreading dynamics of mobile viruses we need to simultaneously model the location (13), the mobility (14-17) and the communication patterns (18-21) of mobile phone users.”
Microscopic Social Influence (2012, background): “Existing research mostly focuses on studying network dynamics within the setting of a single, homogenous network, with examples including diﬀusion and propagation [1], inﬂuence maximization [2, 7], community formation [16], and network evolution [17, 18].”
Community detection in networks: A user guide (2016, methodology): “The simplest approach is to detect the community structure for each individual snapshot, which is a static graph [186,187,74].”
Community detection in Social Media (2012, background): “Figure 9 illustrates six basic transformations that have been identified in a number of studies (Lin et al. 2007; Palla et al. 2007; Schlitter and Falkowski 2009).”
Mapping Change in Large Networks (2008, background): “Researchers have developed a suite of network mapping tools to highlight important features while simplifying the overall structure of social and biological systems [1–6].”
Dynamic social networks promote cooperation in experiments with humans (2011, background): “Exploring cooperation on very large static networks experimentally is an important direction for future study, particularly given the large scale of real-world social networks (18).”
Controlling edge dynamics in complex networks (2011, background): “Considerably less attention has been dedicated to the various types of network dynamics [19,34,46,52] and even less to the problem of controllability [33, 53, 63], i.”
Analyzing communities and their evolutions in dynamic social networks (2009, background): “Because EvolSpec is an evolutionary version of the Normalized Cut (NCut) algorithm in Shi and Malik [2000], we take NCut as our second baseline.”
Community detection in graphs (2009, background): “Scholars have just begun to study this problem (Asur et al., 2007; Chakrabarti et al., 2006; Chi et al., 2007; Fenn et al., 2009; Hopcroft et al., 2004; Kim and Han, 2009; Lin et al., 2008; Palla et al., 2007; Sun et al., 2007) (Section XIII).”
An event-based framework for characterizing the evolutionary behavior of interaction graphs (2007, methodology): “Also, we would like to evaluate the usability of our framework in the verification of popular hypotheses in social network analysis [Palla et al. 2007; Leskovec et al. 2005].”
Computing global structural balance in large-scale signed social networks (2011, background): “…social network theory O nline social networks are examples of large-scale communities of interacting individuals in which local ties between users (friend, fan, colleague, but also friend/foe, trust/distrust, etc.) give rise to a complex, multidimensional web of aggregated social behavior (1 – 4).”
Dynamic reconfiguration of human brain networks during learning (2010, background): “which is the mean autocorrelation over consecutive time steps [52].”
Finding Statistically Significant Communities in Networks (2010, neutral): “Finally, given the recent availability of time-stamped networked datasets, it is now possible to carry out quantitative studies on the dynamics of community structure, about which very little is known [32–37].”
Persistence of social signatures in human communication (2012, background): “[13] Palla G, Barabási A-L, Vicsek T (2007) Quantifying social group evolution.”