Dynamic of Social Networks

• {Leskovec05} Jure Leskovec, Jon Kleinberg, Christos Faloutsos. Graphs over Time: Densification Laws, Shrinking Diameters and Possible Explanations. Proc. of ACM KDD, 2005. effective diameter | graphs densify over time, with the number of edges growing super linearly in the number of nodes | the average distance between nodes often shrinks over time
• {Kumar06} Ravi Kumar, Jasmine Novak, Andrew Tomkins. Structure and Evolution of Online Social Networks. Proc. of ACM KDD, 2006.
• {Leskovec08} Jure Leskovec, Lars Backstrom, Ravi Kumar, Andrew Tomkins. Microscopic Evolution of Social Networks. Proc. of ACM KDD, 2008. microscopic analysis of the edge-by- edge evolution of four large online social networks
• {Mislove08} Alan Mislove, Hema Swetha Koppula, Krishna P. Gummadi, Peter Druschel, and Bobby Bhattacharjee. Growth of the flickr social network. Proc. of ACM WOSN, 2008. reciprocation, preferential attachment, proximity bias in link creation
• {Viswanath09} Bimal Viswanath, Alan Mislove, Meeyoung Cha, Krishna P. Gummadi. On the Evolution of User Interaction in Facebook. Proc. of ACM WOSN, 2009.
• {Zhao12} Xiaohan Zhao, Alessandra Sala, et al. Multi-scale Dynamics in a Massive Online Social Network. Proc. of ACM IMC, 2012. renren evolution
• {Bahmani12} Bahman Bahmani, Ravi Kumar, Mohammad Mahdian, and Eli Upfal. PageRank on an evolving graph. Proc. of ACM KDD, 2012. propose an algorithm that, at any moment in the time and by crawling a small portion of the graph, provides an estimate of the PageRank that is close to the true PageRank of the graph at that moment
• {Gonzalez13} Roberto Gonzalez, Ruben Cuevas, Reza Motamedi, Reza Rejaie, and Angel Cuevas. Google+ or Google-?: dissecting the evolution of the new OSN in its first year. Proc. of WWW, 2013.
• {Liu14} Yabing Liu, Chloe Kliman-Silver, and Alan Mislove. The tweets they are a-changin': Evolution of Twitter users and behavior. Proc. of AAAI ICWSM, 2014. Twitter evolution | a set of over 37 bil- lion tweets spanning over seven years | the spread of Twitter across the globe, the rise of spam and malicious behavior, the rapid adoption of tweeting conventions, and the shift from desktop to mobile usage
• {Yang15} Zhi Yang, Jilong Xue, Christo Wilson, Ben Y. Zhao, and Yafei Dai. Process-driven Analysis of Dynamics in Online Social Interactions. Proc. of ACM COSN, 2015. users invite new friends to interact at a nearly constant rate, prefer to interact with friends with whom they share significant overlaps in social circles, and most social links drop in interaction frequency over time
• {Liu16} Qingyun Liu, Xiaohan Zhao, Walter Willinger, Xiao Wang, Ben Y.Zhao and Haitao Zheng. Self-similarity in Social Network Dynamics. ACM Transactions on Modeling and Performance Evaluation of Computing Systems(ToMPECS), 2016.
• {Qiu16} Jiezhong Qiu, Yixuan Li, Jie Tang, Zheng Lu, Hao Ye, Bo Chen, Qiang Yang, and John E. Hopcroft. The Lifecycle and Cascade of WeChat Social Messaging Groups. Proc. of WWW, 2016.
• {Lo17} C. Lo, J. Cheng, J. Leskovec. Understanding Online Collection Growth Over Time: A Case Study of Pinterest. Proc. of WWW, 2017. board growth can be characterized by short-term fast-paced sprees of activity, and longer breaks between these sprees | a model that can predict whether a board of a given size will grow past its expected median size
• {Aiello17} Luca Maria Aiello, Nicola Barbieri. Evolution of Ego-networks in Social Media with Link Recommendations. Proc. of ACM WSDM, 2017. evolution of ego-networks is bursty, community-driven, and character- ized by subsequent phases of explosive diameter increase, slight shrinking, and stabilization
• {Gu17} Yupeng Gu, Yizhou Sun, Jiaxi Gao. The Co-Evolution Model for Social Network Evolving and Opinion Migration. Proc. of ACM KDD, 2017.
• {Falzon18} Lucia Falzon, Eric Quintane, John Dunn, Garry Robins. Embedding time in positions: Temporal measures of centrality for social network analysis. Social Networks, 2018, 54:168–178.
• {Buß20} Sebastian Buß, Hendrik Molter, Rolf Niedermeier, and Maciej Rymar. Algorithmic Aspects of Temporal Betweenness. Proc. of ACM KDD, 2020.
• {Shukla20} Kshitij Shukla, Sai Charan Regunta, Sai Harsh Tondomker, and Kishore Kothapalli. Efficient parallel algorithms for betweenness- and closeness-centrality in dynamic graphs. Proc. of ACM ICS, 2020.