Structural and Functional Discovery in Dynamic Networks with Non-negative Matrix Factorization
This work addresses the problem of pattern extraction in time-evolving graphs for researchers and practitioners dealing with dynamic network data, representing an incremental improvement by combining matrix factorization with existing methods.
The paper tackles the challenge of exploring and detecting time-varying communities in dynamic graph sequences by developing a matrix factorization model, which is scalable to large weighted networks and accommodates sudden topological changes, as demonstrated on synthetic and real-world data like citation and trade networks.
Time series of graphs are increasingly prevalent in modern data and pose unique challenges to visual exploration and pattern extraction. This paper describes the development and application of matrix factorizations for exploration and time-varying community detection in time-evolving graph sequences. The matrix factorization model allows the user to home in on and display interesting, underlying structure and its evolution over time. The methods are scalable to weighted networks with a large number of time points or nodes, and can accommodate sudden changes to graph topology. Our techniques are demonstrated with several dynamic graph series from both synthetic and real world data, including citation and trade networks. These examples illustrate how users can steer the techniques and combine them with existing methods to discover and display meaningful patterns in sizable graphs over many time points.