By Lei Tang, Huan Liu

This ebook, from a knowledge mining point of view, introduces features of social media, experiences consultant projects of computing with social media, and illustrates linked demanding situations. It introduces easy thoughts, offers state of the art algorithms with easy-to-understand examples, and recommends powerful assessment equipment. specifically, we speak about graph-based neighborhood detection recommendations and plenty of very important extensions that deal with dynamic, heterogeneous networks in social media. We additionally reveal how found styles of groups can be utilized for social media mining. The innovations, algorithms, and techniques provided during this lecture can assist harness the facility of social media and help development socially-intelligent platforms. This ebook is an obtainable creation to the examine of \emph{community detection and mining in social media}. it's a vital examining for college kids, researchers, and practitioners in disciplines and functions the place social media is a key resource of information that piques our interest to appreciate, deal with, innovate, and excel. This ebook is supported by means of extra fabrics, together with lecture slides, the full set of figures, key references, a few toy info units utilized in the e-book, and the resource code of consultant algorithms. The readers are inspired to go to the publication web site http://dmml.asu.edu/cdm/ for the newest details. desk of Contents: Social Media and Social Computing / Nodes, Ties, and impact / group Detection and overview / groups in Heterogeneous Networks / Social Media Mining

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It is a maximum complete subgraph in which all nodes are adjacent to each other. 1, there is a clique of 4 nodes, {5, 6, 7, 8}. Typically, cliques of larger sizes are of much more interest. However, the search for the maximum cliques in a graph is an NP-hard problem. One brute-force approach is to traverse all nodes in a network. For each node, check whether there is any clique of a specified size that contains the node. Suppose we now look at node v . We can maintain a queue of cliques. It is initialized with a clique of one single node {v }.

How to distinguish influence from homophily or confounding? Now with the observation of social networks and historical records of user behavior, we might be able to answer. , 2008). This test assumes we have access to a social network among users, logs of user behaviors with timestamps. 3), we say one actor is active if he performs a target action. Based on the user activity log, we can compute a social correlation coefficient. A simple probabilistic model is adopted to measure the social correlation.

10 O(n3 ) with the Floyd-Warshall algorithm (Floyd, 1962) or O(n2 log n + nm) time complexity with Johnson’s algorithm (Johnson, 1977). The betweenness centrality requires O(nm) computational time following (Brandes, 2001). The eigenvector centrality can be computed with less time and space. Using a simple power method (Golub and Van Loan, 1996) as we did above, the eigenvector centrality can be computed in O(m ) where is the number of iterations. For large-scale networks, efficient computation of centrality measures is critical and requires further research.

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