Machine Learning Department
School of Computer Science, Carnegie Mellon University
Structural Analysis of Large Networks:
Network data (also referred to as relational data, social network data, real graph data) has become ubiquitous, and understanding patterns in this data has become an important research problem. We investigate how interactions in social networks are formed and how these interactions facilitate diffusion, model these behaviors, and apply these findings to real-world problems.
We examined graphs of size up to 16 million nodes, across many domains from academic citation networks, to campaign contributions and actor-movie networks. We also performed several case studies in online social networks such as blogs and message board communities.
Our major contributions are the following: (a) We discover several surprising patterns in network topology and interactions, such as Popularity Decay power law (in-links to a blog post decay with a power law with – 1:5 exponent) and the oscillating size of connected components; (b) We propose generators such as the Butterfly generator that reproduce both established and new properties found in real networks; (c) several case studies, including a proposed method of detecting misstatements in accounting data, where using network effects gave a significant boost in detection accuracy.
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