Node Embedding over Temporal Graphs

In this work, we present a method for node embedding in temporal graphs. We propose an algorithm that learns the evolution of a temporal graph's nodes and edges over time and incorporates this dynamics in a temporal node embedding framework for different graph prediction tasks. We present a joint loss function that creates a temporal embedding of a node by learning to combine its historical temporal embeddings, such that it optimizes per given task (e.g., link prediction). The algorithm is initialized using static node embeddings, which are then aligned over the representations of a node at different time points, and eventually adapted for the given task in a joint optimization. We evaluate the effectiveness of our approach over a variety of temporal graphs for the two fundamental tasks of temporal link prediction and multi-label node classification, comparing to competitive baselines and algorithmic alternatives. Our algorithm shows performance improvements across many of the datasets and baselines and is found particularly effective for graphs that are less cohesive, with a lower clustering coefficient

A Survey on Graph Representation Learning Methods

Graphs representation learning has been a very active research area in recent years. The goal of graph representation learning is to generate graph representation vectors that capture the structure and features of large graphs accurately. This is especially important because the quality of the graph representation vectors will affect the performance of these vectors in downstream tasks such as node classification, link prediction and anomaly detection. Many techniques are proposed for generating effective graph representation vectors. Two of the most prevalent categories of graph representation learning are graph embedding methods without using graph neural nets (GNN), which we denote as non-GNN based graph embedding methods, and graph neural nets (GNN) based methods. Non-GNN graph embedding methods are based on techniques such as random walks, temporal point processes and neural network learning methods. GNN-based methods, on the other hand, are the application of deep learning on graph data. In this survey, we provide an overview of these two categories and cover the current state-of-the-art methods for both static and dynamic graphs. Finally, we explore some open and ongoing research directions for future work

Representation learning on heterogeneous spatiotemporal networks

“The problem of learning latent representations of heterogeneous networks with spatial and temporal attributes has been gaining traction in recent years, given its myriad of real-world applications. Most systems with applications in the field of transportation, urban economics, medical information, online e-commerce, etc., handle big data that can be structured into Spatiotemporal Heterogeneous Networks (SHNs), thereby making efficient analysis of these networks extremely vital. In recent years, representation learning models have proven to be quite efficient in capturing effective lower-dimensional representations of data. But, capturing efficient representations of SHNs continues to pose a challenge for the following reasons: (i) Spatiotemporal data that is structured as SHN encapsulate complex spatial and temporal relationships that exist among real-world objects, rendering traditional feature engineering approaches inefficient and compute-intensive; (ii) Due to the unique nature of the SHNs, existing representation learning techniques cannot be directly adopted to capture their representations. To address the problem of learning representations of SHNs, four novel frameworks that focus on their unique spatial and temporal characteristics are introduced: (i) collective representation learning, which focuses on quantifying the importance of each latent feature using Laplacian scores; (ii) modality aware representation learning, which learns from the complex user mobility pattern; (iii) distributed representation learning, which focuses on learning human mobility patterns by leveraging Natural Language Processing algorithms; and (iv) representation learning with node sense disambiguation, which learns contrastive senses of nodes in SHNs. The developed frameworks can help us capture higher-order spatial and temporal interactions of real-world SHNs. Through data-driven simulations, machine learning and deep learning models trained on the representations learned from the developed frameworks are proven to be much more efficient and effective”--Abstract, page iii