Big graph mining for the web and social media

U. Kang, Leman Akoglu, Duen Horng Chau
2014 Proceedings of the 7th ACM international conference on Web search and data mining - WSDM '14  
Graphs are everywhere in our lives: social networks, the World Wide Web, biological networks, and many more. These graphs are growing at unprecedented rate, now exceeding billions of nodes and edges. What are the patterns and anomalies in such massive graphs? How to design scalable algorithms to find them? What visual analytics techniques to use to make sense of such massive graphs? And what kind of real-world problems, associated with the Web and social media, can we solve with such tools?
more » ... e are exactly the goals of this tutorial. We start with important graph algorithms that are central to graph mining and pattern discoveries, including graphbased anomaly detection techniques (complement of pattern discoveries) that are playing increasingly important role in exploratory analysis and helping users gain insight into data. Then we describe how to scale up these techniques to massive graphs with billions of nodes. Finally, we discuss how our aforementioned techniques help solve real-world problems that make impact to society (e.g., fraud/malware detection, recommendations, community detection).
doi:10.1145/2556195.2556198 dblp:conf/wsdm/KangAC14 fatcat:fbe7ciirlzd3xm42h3y67bw77i