Learning dynamic processes over graphs
Learning dynamic processes over graphs
Graphs appear as a versatile representation of information across domains spanning social networks, biological networks, transportation networks, molecular structures, knowledge networks, web information network and many more. Graphs represent heterogeneous information about the real-world entities and complex relationships between them in a very succinct manner. At the same time, graphs exhibit combinatorial, discrete and non- Euclidean properties in addition to being inherently sparse and incomplete which poses several challenges to techniques that analyze and study these graph structures. There exist various approaches across different fields spanning network science, game theory, stochastic process and others that provide excellent theoretical and analytical tools with interpretability benefits to analyze these networks. However, such approaches do not learn from data and make assumptions about real-world that capture only subset of properties. More importantly, they do not support predictive capabilities critical for decision making applications. In this thesis, we develop novel data driven learning approaches that incorporate useful inductive biases inspired from these classical approaches. The resulting learning approaches exhibit more general properties that go beyond conventional probabilistic assumptions and allow for building transferable and interpretable modules. We build these approaches anchored around two fundamental questions: (i) (Formation Pro- cess) How do these networks come into existence? and (ii) (Temporal Evolution Process) How do real-world networks evolve over time? First, we focus on the challenge of learning in a setting with highly sparse and in- complete knowledge graphs, where it is important to leverage multiple input graphs to sup- port accurate performance for variety of downstream applications such as recommendation, search and question-answering systems. Specifically, we develop a large-scale multi-graph deep relational learning framework that identifies entity linkage as a vital component of data fusion and learns to jointly perform representation learning and graph linkage across multiple graphs with applications to relational reasoning and knowledge construction. Next, we consider networks that evolve over time and propose a generative model of dynamic graphs that is useful to encode evolving network information into low-dimensional representations that facilitate accurate downstream event prediction tasks. Our approach relies on the coevolution principle of network structure evolution and network activities being tightly couple processes and develops a multi time scale temporal point process formulation parameterized by a recurrent architecture comprising of a novel Temporal Attention mechanism. Representation learning is posed as a latent mediation process – observed network processes evolve the state of nodes, while this node evolution governs future dynamics of observed processes and applied to downstream dynamic link prediction tasks and time prediction of future realizations (events) of both observed processes. Finally, we investigate the implication of adopting the optimization perspective of net- work formation mechanisms for building learning approaches for graph structured data. In this work, we first focus on global mechanisms that govern the formation of links in the network and build an inverse reinforcement learning based algorithm to jointly discover latent mechanisms directly from observed data, optimization of which enables a graph construction procedure capable of producing graphs with properties similar to observed data. Such an approach facilitates transfer and generalization properties and has been applied to variety of real-world graphs. In the last part, we consider the settings where the agents forming links are strategic and build a learnable model of network emergence games that jointly discovers the underlying payoff mechanisms and strategic profiles of agents from the data. This approach enables learning interpretable and transferable payoffs while the learned game as a model facilitates strategic prediction tasks, both of which are applied to several real world networks.