{Newman02} M. E. J. Newman, Stephanie Forrest, and Justin Balthrop. Email networks and the spread of computer viruses. Phys. Rev. E, 2002, 66, 035101(R). if we can protect a suitably selected 10% of the vertices in the net- work, almost all vertices would become immune to an epidemicPDF
{Leskovec06} J. Leskovec, Christos Faloutsos. Sampling from Large Graphs. In Proc. of ACM SIGKDD, 2006. comparison among different graph sampling algorithms; scale-down sampling & back-in-time sampling
{Mislove07} Alan Mislove, Massimiliano Marcon, Krishna P. Gummadi, Peter Druschel, Bobby Bhattacharjee. Measurement and Analysis of Online Social Networks. Proc. of ACM IMC, 2007.
{Kwak09} H. Kwak, Y. Choi, Y.H. Eom, and et al. Mining Communities in Networks: a Solution for Consistency and Its Evaluation. In Proc. of ACM IMC, 2009.
{Wilson09} Christo Wilson, Bryce Boe, Alessandra Sala, Krishna P. N. Puttaswamy and Ben Y. Zhao. User Interactions in Social Networks and their Implications. In Proc. of ACM EuroSys 2009. interaction graph
{Centola10} D. Centola. The Spread of Behavior in an Online Social Network Experiment. Science, 329(5996):1194–1197, 2010. the effects of network structure on diffusion by studying the spread of health behavior through artificially structured online communities
{Jiang10} Jing Jiang, Christo Wilson, Xiao Wang, Peng Huang, Wenpeng Sha, Yafei Dai and Ben Y. Zhao. Understanding Latent Interactions in Online Social Networks. In Proc. of ACM IMC, 2010. latent graph
{Sala10} Alessandra Sala, Lili Cao, Christo Wilson, Robert Zablit, Haitao Zheng and Ben Y. Zhao. Measurement-calibrated Graph Models for Social Network Experiments. In Proc. of WWW, 2010.
{Ugander12} Johan Ugander, Brian Karrer, Lars Backstrom, Cameron Marlow. The Anatomy of the Facebook Social Graph. arXiv:1111.4503, 2011.
{Myers14} Seth A. Myers, Aneesh Sharma, Pankaj Gupta, and Jimmy Lin. Information Network or Social Network? The Structure of the Twitter Follow Graph. WWW Workshop, 2014. a snapshot of the Twitter follow graph from the second half of 2012 with 175 million active users and approximately twenty billion edges
{Gabielkov14} Maksym Gabielkov, Ashwin Rao, Arnaud Legout. Studying Social Networks at Scale: Macroscopic Anatomy of the Twitter Social Graph. Proc. of ACM SIGMETRICS, 2014. macroscopic structure
{Leskovec15} J. Leskovec, E. Horvitz. Geospatial Structure of a Planetary-Scale Social Network. IEEE Transactions on Computational Social Systems (TCSS), 2015. geographical distances of edges | geographic navigation (navigating through the periphery of the network is relatively easy, but that it is difficult to navigate through the core of the network composed of high-degree nodes)
{Zhang15} Jing Zhang, Jie Tang, Cong Ma, Hanghang Tong, Yu Jing, Juanzi Li. Panther: Fast Top-k Similarity Search on Large Networks. Proc. of ACM KDD, 2015. obtain top-k similar vertices for any vertex in a large network
{Han16} Wentao Han, Xiaowei Zhu, Ziyan Zhu, Wenguang Chen, Weimin Zheng, and Jianguo Lu. A comparative analysis on Weibo and Twitter. Tsinghua Science and Technology, 2016, 21(1):1-16. Weibo users have a higher diversity index, higher Gini index, but a lower reciprocity and clustering coefficient for most of the nodes
{Leskovec16} Jure Leskovec and Rok Sosič. SNAP: A General-Purpose Network Analysis and Graph-Mining Library. ACM Trans. Intell. Syst. Technol. 8, 1, Article 1 (July 2016), 20 page.
{Shin18} Kijung Shin, Tina Eliassi-Rad, Christos Faloutsos. Patterns and anomalies in k-cores of real-world graphs with applications. Knowledge and Information Systems, 2018, 54(3):677-710. k-cores of real-world graphsPDF
{Chen18} Xiaowei Chen, John C.S. Lui. Mining Graphlet Counts in Online Social Networks. ACM Transactions on Knowledge Discovery from Data, 2018, 12(4): Article 41.
{Peel18} Leto Peel, Jean-Charles Delvenne, and Renaud Lambiotte. Multiscale mixing patterns in networks. PNAS, 2018, 115(16):4057-4062. introduce an approach to localize this global measure so that wecan describe the assortativity, across multiple scales, at the nodelevel.
{Cao20} Hancheng Cao, Zhilong Chen,Fengli Xu, Tao Wang, Yujian Xu, Lianglun Zhang, Yong Li. When Your Friends Become Sellers:An Empirical Study of Social Commerce Site Beidian. Proc. of AAAI ICSWM, 2020. We first analyzed the topological structure of the Beidian platform and highlighted its decentralized nature. We then studied the site’s rapid growth and its growth mechanism via invitation cascade. Finally, we investigated purchasing behav-ior on Beidian, where we focused on user proximity and loyalty, which contributes to the site's high conversion rate
Centrality
{Schocha17} David Schocha, Thomas W. Valente, Ulrik Brandes. Correlations among centrality indices and a class of uniquely ranked graphs. Social Networks, 2017, 50:46-54.
{Das18} Kousik Das, Sovan Samanta, Madhumangal Pal. Study on centrality measures in social networks: a survey. Social Network Analysis and Mining, 2018, 8:Article 13.
{Fan19} Changjun Fan, Li Zeng, Yuhui Ding, Muhao Chen, Yizhou Sun, and Zhong Liu. Learning to Identify High Betweenness Centrality Nodes from Scratch: A Novel Graph Neural Network Approach. Proc. of ACM CIKM, 2019.
Ego networks
{Everett05} Martin Everett, Stephen P. Borgatti. Ego network betweenness. Social Networks, 2005, 27(1):31-38. PDF
{Hsu20} Bay-Yuan Hsu, Chih-Ya Shen, and Ming-Yi Chang. WMEgo: Willingness Maximization for Ego Network Data Extraction in Online Social Networks. Proc. of ACM CIKM, 2020.
Community
{Newman06} M. E. J. Newman. Modularity and community structure in networks. PNAS, 2006, 103(23):8577-8582. modularity
{Yang15} Jaewon Yang and Jure Leskovec. Defining and evaluating network communities based on ground-truth. Knowledge and Information Systems, 2015, 42(1):181-213.
{Ahajjam18} Sara Ahajjam, Mohamed El Haddad, Hassan Badir. A new scalable leader-community detection approach for community detection in social networks. Social Networks, 2018, 54:41-49.
Graph Sampling
{Gjoka10} Minas Gjoka, Maciej Kurant, Carter T Butts, Athina Markopoulou. Walking in Facebook: A Case Study of Unbiased Sampling of OSNs. In Proc. of IEEE Infocom, 2010. MHRW
{Ribeiro10} Bruno Ribeiro and Don Towsley. Estimating and Sampling Graphs with Multidimensional Random Walks. In Proc. of ACM IMC, 2010.
{Kurant11} Maciej Kurant, Minas Gjoka, Carter T. Butts, Athina Markopoulou. Walking on a Graph with a Magnifying Glass. In Proc. of ACM SIGMETRICS, 2011.
{Gjoka11} Minas Gjoka, Maciej Kurant, Carter T. Butts, Athina Markopoulou. Practical Recommendations on Crawling Online Social Networks. IEEE Journal on Selected Areas in Communications, 2011, 29(9):1872-1892.
Hypergraph
{Benson18} Austin R. Benson, Rediet Abebe, Michael T. Schaub, Ali Jadbabaie, and Jon Kleinberg. Simplicial closure and higher-order link prediction. PNAS, 2018, 115(48):E11221–E11230.'
{Ruggeri23} Nicolò Ruggeri, Martina Contisciani, Federico Battiston, Caterina De Bacco. Community detection in large hypergraphs. Science Advances, 2023, 9(28). Our approach recovers community structure with accuracy exceeding that of currently available state-of-the-art algorithms, as tested in synthetic benchmarks with both hard and overlapping ground-truth partitions.