Latent Self-Exciting Point Process Model for Spatial-Temporal Networks

We propose a latent self-exciting point process model that describes geographically distributed interactions between pairs of entities. In contrast to most existing approaches that assume fully observable interactions, here we consider a scenario where certain interaction events lack information about participants. Instead, this information needs to be inferred from the available observations. We develop an efficient approximate algorithm based on variational expectation-maximization to infer unknown participants in an event given the location and the time of the event. We validate the model on synthetic as well as real-world data, and obtain very promising results on the identity-inference task. We also use our model to predict the timing and participants of future events, and demonstrate that it compares favorably with baseline approaches.Comment: 20 pages, 6 figures (v3); 11 pages, 6 figures (v2); previous version appeared in the 9th Bayesian Modeling Applications Workshop, UAI'1

Learning From Attributed Networks - Embedding, Theory, and Interactions

Networks are widely adopted to represent the relations between objects in many disciplines. In real-world scenarios, nodes are often associated with a rich set of data describing their characteristics, such as social networks with user-generated content. We model these systems as attributed networks. They are a unique data structure that simultaneously assesses networks and node individual attributes or content, and pervasive in practice. In this thesis, I present effective, scalable, and human-centric learning algorithms for attributed networks, to enable their actionable patterns to be easily accessible to data consumers. Most machine learning algorithms make default assumptions that instances are independent of each other, and their features are in Euclidean space. These do not hold for attributed networks. To bridge the gap, attributed network embedding (ANE) aims to learn low-dimensional vectors to represent nodes, such that actionable patterns in original networks and node attributes can be preserved. The learned representations could be directly leveraged by off-the-shelf machine learning algorithms as feature vectors or hidden layers to conduct different tasks. I systematically developed a series of ANE algorithms, which could be categorized into four classes, including coupled spectral embedding, coupled-factorizations-based embedding, joint-random-walks-based embedding, and graph neural networks, to bridge the gap between large-scale networked data and off-the-shelf machine learning algorithms. On this basis, I developed interactive embedding to involve domain experts in advancing the ANE. Experts have a better cognition in the latent information such as domain knowledge and hidden relations. So we could learn from them and incorporate their knowledge into ANE. My research enables data scientists and domain experts to effectively utilize the abundant but complex information. It broadly impacts fields such as Information Retrieval, Social Computing, Health Informatics, and Bioinformatics

Identifying Missing Node Information in Social Networks

In recent years, social networks have surged in popularity as one of the main applications of the Internet. This has generated great interest in researching these networks by various fields in the scientific community. One key aspect of social network research is identifying important missing information which is not explicitly represented in the network, or is not visible to all. To date, this line of research typically focused on what connections were missing between nodes, or what is termed the "Missing Link Problem". This paper introduces a new Missing Nodes Identification problem where missing members in the social network structure must be identified. Towards solving this problem, we present an approach based on clustering algorithms combined with measures from missing link research. We show that this approach has beneficial results in the missing nodes identification process and we measure its performance in several different scenarios