Renhe Jiang is a full-time lecturer at Center for Spatial Information Science, The University of Tokyo. He received his B.S. degree in Software Engineering from Dalian University of Technology, China, in 2012, M.S. degree in Information Science from Nagoya University, Japan, in 2015, and Ph.D. degree in Civil Engineering from The University of Tokyo, Japan, in 2019. From 2019 to 2022, he was an assistant professor at Information Technology Center, The University of Tokyo. His research interests include data mining and machine learning, especially spatiotemporal data science, spatiotemporal AI, multivariate time series, urban computing, and intelligent transportation system.
Spatio-Temporal Meta-Graph Learning for Traffic Forecasting (AAAI 2023). Traffic forecasting as a canonical task of multivariate time series forecasting has been a significant research topic in AI community. To address the spatio-temporal heterogeneity and non-stationarity implied in the traffic stream, in this study, we propose Spatio-Temporal Meta-Graph Learning as a novel Graph Structure Learning mechanism on spatio-temporal data. Specifically, we implement this idea into Meta-Graph Convolutional Recurrent Network (MegaCRN) by plugging the Meta-Graph Learner powered by a Meta-Node Bank into GCRN encoder-decoder. We conduct a comprehensive evaluation on two benchmark datasets (i.e., METR-LA and PEMS-BAY) and a new large-scale traffic speed dataset called EXPY-TKY that covers 1843 expressway road links in Tokyo. Our model outperformed the state-of-the-arts on all three datasets. Besides, through a series of qualitative evaluations, we demonstrate that our model can explicitly disentangle the road links and time slots with different patterns and be robustly adaptive to any anomalous traffic situations. Codes and datasets are available at https://github.com/deepkashiwa20/MegaCRN.
DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction (CIKM 2021). Nowadays, with the rapid development of IoT (Internet of Things) and CPS (Cyber-Physical Systems) technologies, big spatiotemporal data are being generated from mobile phones, car navigation systems, and traffic sensors. By leveraging state-of-the-art deep learning technologies on such data, urban traffic prediction has drawn a lot of attention in AI and Intelligent Transportation System community. The problem can be uniformly modeled with a 3D tensor (T, N, C), where T denotes the total time steps, N denotes the size of the spatial domain (i.e., mesh-grids or graph-nodes), and C denotes the channels of information. According to the specific modeling strategy, the state-of-the-art deep learning models can be divided into three categories: grid-based, graph-based, and multivariate time-series models. In this study, we first synthetically review the deep traffic models as well as the widely used datasets, then build a standard benchmark to comprehensively evaluate their performances with the same settings and metrics. Our study named DL-Traff is implemented with two most popular deep learning frameworks, i.e., TensorFlow and PyTorch, which is already publicly available as two GitHub repositories https://github.com/deepkashiwa20/DL-Traff-Grid and https://github.com/deepkashiwa20/DL-Traff-Graph. With DL-Traff, we hope to deliver a useful resource to researchers who are interested in spatiotemporal data analysis.
[April 20th, 2023]
Graph Neural Networks for Link Prediction with Subgraph Sketching , ICLR 2023 Notable top 5%
Presenter: Benjamin Paul Chamberlain and Sergey Shirobokov
Many Graph Neural Networks (GNNs) perform poorly compared to simple heuristics on Link Prediction (LP) tasks. This is due to limitations in expressive power such as the inability to count triangles (the backbone of most LP heuristics) and because they can not distinguish automorphic nodes (those having identical structural roles). Both expressiveness issues can be alleviated by learning link (rather than node) representations and incorporating structural features such as triangle counts. Since explicit link representations are often prohibitively expensive, recent works resorted to subgraph-based methods, which have achieved state-of-the-art performance for LP, but suffer from poor efficiency due to high levels of redundancy between subgraphs. We analyze the components of subgraph GNN (SGNN) methods for link prediction. Based on our analysis, we propose a novel full-graph GNN called ELPH (Efficient Link Prediction with Hashing) that passes subgraph sketches as messages to approximate the key components of SGNNs without explicit subgraph construction. ELPH is provably more expressive than Message Passing GNNs (MPNNs). It outperforms existing SGNN models on many standard LP benchmarks while being orders of magnitude faster. However, it shares the common GNN limitation that it is only efficient when the dataset fits in GPU memory. Accordingly, we develop a highly scalable model, called BUDDY, which uses feature precomputation to circumvent this limitation without sacrificing predictive performance. Our experiments show that BUDDY also outperforms SGNNs on standard LP benchmarks while being highly scalable and faster than ELPH.
[April 27th, 2023]
Temporal Knowledge Graph Reasoning with Historical Contrastive Learning
Presenter: Yi Xu, Shanghai Jiao Tong University
Yi Xu is a 3th year Ph.D. candidate at Shanghai Jiao Tong University under the supervision of Prof. Luoyi Fu and Prof. Xinbing Wang. His research interests include natural language processing, knowledge graphs and data mining. Specifically, He is devoted to the research of large language models and temporal knowledge graph.
Temporal knowledge graph, serving as an effective way to store and model dynamic relations, shows promising prospects in event forecasting. However, most temporal knowledge graph reasoning methods are highly dependent on the recurrence or periodicity of events, which brings challenges to inferring future events related to entities that lack historical interaction. In fact, the current moment is often the combined effect of a small part of historical information and those unobserved underlying factors. To this end, we propose a new event forecasting model called Contrastive Event Network (CENET), based on a novel training framework of historical contrastive learning. CENET learns both the historical and non-historical dependency to distinguish the most potential entities that can best match the given query. Simultaneously, it trains representations of queries to investigate whether the current moment depends more on historical or non-historical events by launching contrastive learning. The representations further help train a binary classifier whose output is a boolean mask to indicate related entities in the search space. During the inference process, CENET employs a mask-based strategy to generate the final results. We evaluate our proposed model on five benchmark graphs. The results demonstrate that CENET significantly outperforms all existing methods in most metrics, achieving at least 8.3% relative improvement of Hits@1 over previous state-of-the-art baselines on event-based datasets.
[Feb. 16th, 2023]
Neighborhood-aware Scalable Temporal Network Representation Learning , LOG 2022 Conference Best Paper
Presenter: Yuhong Luo, University of Massachusetts
Yuhong Luo is a first year master student at UMass Amherst. Prior to that, he worked as a software engineer at Airbnb and worked with professor Pan Li of Purdue University as a research intern. His research interest includes ML, graph representation learning, ML fairness, etc.
Temporal networks have been widely used to model real-world complex systems such as financial systems and e-commerce systems. In a temporal network, the joint neighborhood of a set of nodes often provides crucial structural information useful for predicting whether they may interact at a certain time. However, recent represen- tation learning methods for temporal networks often fail to extract such information or depend on online construction of structural features, which is time-consuming. To address the issue, this work proposes Neighborhood-Aware Temporal network model (NAT). For each node in the network, NAT abandons the commonly-used one-single-vector-based representation while adopting a novel dictionary-type neighborhood representation. Such a dictionary representation records a down- sampled set of the neighboring nodes as keys, and allows fast construction of struc- tural features for a joint neighborhood of multiple nodes. We also design a dedicated data structure termed N-cache to support parallel access and update of those dic- tionary representations on GPUs. NAT gets evaluated over seven real-world large- scale temporal networks. NAT not only outperforms all cutting-edge baselines by averaged 1.2%↑ and 4.2%↑ in transductive and inductive link prediction accuracy, respectively, but also keeps scalable by achieving a speed-up of 4.1-76.7× against the baselines that adopt joint structural features and achieves a speed-up of 1.6-4.0× against the baselines that cannot adopt those features. The link to the code: https: //github.com/Graph-COM/Neighborhood-Aware-Temporal-Network.
[Feb. 23rd, 2023]
Direct Embedding of Temporal Network Edges via Time-Decayed Line Graphs , ICLR 2023
Presenter: Sudhanshu (Dan) Chanpuriya, University of Massachusetts, Amherst
Sudhanshu Chanpuriya is a PhD student at UMass Amherst's Theoretical Computer Science Group, where he is advised by Cameron Musco. His research interests lie at the intersection of machine learning and spectral graph theory. Prior to joining UMass, he studied computer science and engineering physics at Dartmouth College.
Temporal networks model a variety of important phenomena involving timed interactions between entities. Existing methods for machine learning on temporal networks generally exhibit at least one of two limitations. First, time is assumed to be discretized, so if the time data is continuous, the user must determine the discretization and discard precise time information. Second, edge representations can only be calculated indirectly from the nodes, which may be suboptimal for tasks like edge classification. We present a simple method that avoids both shortcomings: construct the line graph of the network, which includes a node for each interaction, and weigh the edges of this graph based on the difference in time between interactions. From this derived graph, edge representations for the original network can be computed with efficient classical methods. The simplicity of this approach facilitates explicit theoretical analysis: we can constructively show the effectiveness of our method's representations for a natural synthetic model of temporal networks. Empirical results on real-world networks demonstrate our method's efficacy and efficiency on both edge classification and temporal link prediction.
[March 2nd, 2023]
Neural Temporal Walks: Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs, NeurIPS 2022
Presenter: Ming Jin, Monash University
Ming Jin is a Ph.D. Candidate in Computer Science at Monash University, where he is currently being supervised by Prof. Shirui Pan and A/Prof. Yuan-Fang Li. He is also a Research Staff at Metso Outotec and a Research Assistant at Monash University. Ming completed his Bachelor's and Master's degrees at Hebei University of Technology and The University of Melbourne in 2017 and 2019, respectively. He has published over ten top-ranked conference and journal papers and regularly served as a PC member and reviewer of major AI/ML/DM conferences and journals, including IJCAI, PAKDD, TNNLS, TKDE, and more. Ming's research interests mainly focus on graph neural networks and geometric deep learning, with a particular emphasis on temporal settings such as dynamic graph neural networks. He is particularly passionate about applying these techniques to tackle practical challenges such as time series forecasting, industrial process modeling, and anomaly detection.
Continuous-time dynamic graphs naturally abstract many real-world systems, such as social and transactional networks. While the research on continuous-time dynamic graph representation learning has made significant advances recently, neither graph topological properties nor temporal dependencies have been well-considered and explicitly modeled in capturing dynamic patterns. In this paper, we introduce a new approach, Neural Temporal Walks (NeurTWs), for representation learning on continuous-time dynamic graphs. By considering not only time constraints but also structural and tree traversal properties, our method conducts spatiotemporal-biased random walks to retrieve a set of representative motifs, enabling temporal nodes to be characterized effectively. With a component based on neural ordinary differential equations, the extracted motifs allow for irregularly-sampled temporal nodes to be embedded explicitly over multiple different interaction time intervals, enabling the effective capture of the underlying spatiotemporal dynamics. To enrich supervision signals, we further design a harder contrastive pretext task for model optimization. Our method demonstrates overwhelming superiority under both transductive and inductive settings on six real-world datasets.
[March 9th, 2023]
Learnable Spectral Wavelets on Dynamic Graphs to Capture Global Interactions , AAAI 2023
Presenter: Anson Bastos, Indian Institue of Technology, Hyderabad (IITH, India) and Abhishek Nadgeri, RWTH Aachen, Germany
Anson Basto is a PhD student at the Indian Institue of Technology, Hyderabad (IITH, India) under the supervision of Dr Manish Singh (IITH, India) and Dr Toyotaro Suzamura (The University of Tokyo, Japan). His research interests/works are in the areas of Geometric machine learning, Graph Signal Processing, Topology, Knowledge graphs etc.
Abhishek Nadgeri is currently pursuing his master’s at RWTH Aachen, Germany, his research interests are Graph ML, time series and NLP.
Learning on evolving(dynamic) graphs has caught the attention of researchers as static methods exhibit limited performance in this setting. The existing methods for dynamic graphs learn spatial features by local neighborhood aggregation, which essentially only captures the low pass signals and local interactions. In this work, we go beyond current approaches to incorporate global features for effectively learning representations of a dynamically evolving graph. We propose to do so by capturing the spectrum of the dynamic graph. Since static methods to learn the graph spectrum would not consider the history of the evolution of the spectrum as the graph evolves with time, we propose a novel approach to learn the graph wavelets to capture this evolving spectra. Further, we propose a framework that integrates the dynamically captured spectra in the form of these learnable wavelets into spatial features for incorporating local and global interactions. Experiments on eight standard datasets show that our method significantly outperforms related methods on various tasks for dynamic graphs.
[March 16th, 2023]
Do We Really Need Complicated Model Architectures For Temporal Networks? , ICLR 2023
Presenter: Weilin Cong, Pennsylvania State University
Weilin Cong is a 4th year Ph.D. candidate at Penn State University. His research focuses on both the fundamental problems in graph representation learning, including optimization, generalization, expressive power, and model architecture design. He has published as the first author in AI conferences NeurIPS, ICLR, AISTATS, KDD and SDM on graph representation learning.
Recurrent neural network (RNN) and self-attention mechanism (SAM) are the de facto methods to extract spatial-temporal information for temporal graph learning. Interestingly, we found that although both RNN and SAM could lead to a good per-formance, in practice neither of them is always necessary. In this paper, we propose GraphMixer , a conceptually and technically simple architecture that consists of three components: 1 a link-encoder that is only based on multi-layer perceptrons (MLP) to summarize the information from temporal links, 2 a node-encoder that is only based on neighbor mean-pooling to summarize node information, and 3 an MLP-based link classifier that performs link prediction based on the outputs of the encoders. Despite its simplicity, GraphMixer attains an outstanding performance on temporal link prediction benchmarks with faster convergence and better generalization performance. These results motivate us to rethink the importance of simpler model architecture. Code is in the supplementary.
Farimah Poursafaei (she/her) is a PostDoc at McGill University and Mila. She conducts research on dynamic graph neural networks, and temporal graphs. She completed her PhD at McGill University in Computer Engineering.
During her PhD, she was working on anomaly detection on cryptocurrency transactions networks. She served as the Reviewing Chair in Temporal Graph Learning Workshop @ NeurIPS 2022.
Google Scholar, Linkedin
Julia Gastinger (she/her) is a research scientist in the AI Innovations group at NEC Laboratories Europe and a Ph.D. student at Mannheim University, supervised by Professor Heiner Stuckenschmidt.
Her research primarily focuses on graph-based Machine Learning – she is interested in how to incorporate the time aspect in knowledge graph representations.
Prior to joining NEC Laboratories Europe, Julia graduated with a master’s degree from Stuttgart University, where she studied Engineering Cybernetics with a focus on Autonomous Systems and Control Theory.
Google Scholar, LinkedIn, Nec Laboratories
Most Recent Publication: On the Evaluation of Methods for Temporal Knowledge Graph Forecasting
Shenyang Huang (he/him) is a PhD student at McGill University and Mila, focusing on temporal graph learning (supervised by Prof. Reihaneh Rabbany and Prof. Guillaume Rabusseau).
He is interested in representation learning on temporal graphs, anomaly detection and graph representation learning. He was the Organization Chair for the Temporal Graph Learning Workshop @ NeurIPS 2022.
His previous research includes change point detection on temporal graphs, COVID-19 disease modeling with temporal contact graphs and link prediction on temporal graphs. He also enjoys writing medium blog posts about temporal graph learning.
Website, Google Scholar, Twitter, Linkedin