Models and algorithms for event-driven networks.

摘要

Many real-world systems can be represented as networks driven by discrete events, each event identified by the time at which it occurs and the parties involved. An event could be a meeting, a stock trade, a phone call, an email, a gang fight, an online or off-line purchase, a blog post, a conference, or the transmission of an IP packet. Innovations in technology have increased our ability to collect massive amounts of digital data from such networks, which presents both new opportunities and new challenges. In this work, we develop new theoretical models and efficient algorithms that leverage the temporal and relational information inherent in the data to better understand and analyze real-world networks. In particular, we consider three problems: (1) detecting correlated events in communication networks; (2) discovering functional communities; and (3) modeling collaboration in academia.;First we present a new stochastic model for event-driven networks, and with it develop two algorithms -- a streaming local algorithm, and an efficient global algorithm -- to detect statistically correlated activity. We demonstrate that our approach, which models each communication channel as its own stochastic process, is better able to accommodate the temporal variability present in real-world communication networks than existing methods.;Next we study diffusion processes in information networks, identifying functional communities as groups of individuals who participate in the dissemination of common content by reframing the problem as one of co-clustering sparse matrices. We propose a new co-clustering algorithm that does not require user-specified parameters, and leverages sparsity in the data to run in sublinear time in the size of the matrix.;Finally, we build a game-theoretic model for academic collaboration, representing the academic environment as a repeated game in which each researcher tries to maximize his or her academic success. We find analytically that limitations of existing collaboration models may result in misleading predictions about people's behavior.