Social Network Analysis: Online Anomaly Detection and Graphical Model Selection

نویسندگان

  • Corinne Horn
  • Rebecca Willett
چکیده

There is a demand for computational methods that can extract meaningful patterns from social networks in real time. However, these networks can be extremely large and volatile, and brute force algorithms for high-dimensional data analysis are intractable as high costs and poor runtime preclude many real-world applications. I present two online (sequentially updating) strategies that from learn from observations of a network in order to accomplish some task. Furthermore, both online methods analyze binary data, making them memory-efficient and accommodating to exceptionally large datasets. One method detects unusual or deviant behavior while utilizing expert feedback to adjust to a continuously evolving definition of alarming behavior. The second method learns relationships among individuals (via graphical model selection) that underpin otherwise indiscernible social patterns. In this paper I primarily focus on my contributions to the two projects. I applied the theoretical framework from the anomaly detection scheme to two real-world datasets. Then, after familiarizing myself with relevant literature, I formulated, analyzed, and tested my graphical model selection algorithm on simulated and real world data.

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تاریخ انتشار 2011