Graph neural networks (GNNs) realize great success in graph learning but suffer from performance loss when meeting heterophily, i.e. neighboring nodes are dissimilar, due to their local and uniform aggregation. Existing attempts of heterophilous GNNs incorporate long-range or global aggregations to distinguish nodes in the graph. However, these aggregations usually require iteratively maintaining and updating full-graph information, which limits their efficiency when applying to large-scale graphs. In this paper, we propose SIGMA, an efficient global heterophilous GNN aggregation integrating the structural similarity measurement SimRank. Our theoretical analysis illustrates that SIGMA inherently captures distant global similarity even under heterophily, that conventional approaches can only achieve after iterative aggregations. Furthermore, it enjoys efficient one-time computation with a complexity only linear to the node set size$O(n)$. Comprehensive evaluation demonstrates that SIGMA achieves state-of-the-art performance with superior aggregation and overall efficiency. Notably, it obtains 5 x acceleration on the large-scale heterophily dataset pokec with over 30 million edges compared to the best baseline aggregation.

With recent advancements in graph neural networks (GNNs), spectral GNNs have received increasing popularity by virtue of their ability to retrieve graph signals in the spectral domain. These models feature uniqueness in efficient computation as well as rich expressiveness, which stems from advanced management and profound understanding of graph data. However, few systematic studies have been conducted to assess spectral GNNs, particularly in benchmarking their efficiency, memory consumption, and effectiveness in a unified and fair manner. There is also a pressing need to select spectral models suitable for learning specific graph data and deploying them to massive web-scale graphs, which is currently constrained by the varied model designs and training settings. In this work, we extensively benchmark spectral GNNs with a focus on the spectral perspective, demystifying them as spectral graph filters. We analyze and categorize 35 GNNs with 27 corresponding filters, spanning diverse formulations and utilizations of the graph data. Then, we implement the filters within a unified spectral-oriented framework with dedicated graph computations and efficient training schemes. In particular, our implementation enables the deployment of spectral GNNs over million-scale graphs and various tasks with comparable performance and less overhead. Thorough experiments are conducted on the graph filters with comprehensive metrics on effectiveness and efficiency, offering novel observations and practical guidelines that are only available from our evaluations across graph scales. Different from the prevailing belief, our benchmark reveals an intricate landscape regarding the effectiveness and efficiency of spectral graph filters, demonstrating the potential to achieve desirable performance through tailored spectral manipulation of graph data.

Graph representation learning is an emerging task for effectively embedding graph-structured data with learned features. Among them, Subgraph-based GRL (SGRL) methods have demonstrated better scalability and expressiveness for large-scale tasks. The core challenge of applying SGRL to dynamic graphs lies in accommodating the extraction of subgraphs to evolving data with efficient computation. To address the efficiency bottleneck, we propose GENTI, a GPU-oriented SGRL algorithm for dynamic graphs. Our approach mainly improves the critical subgraph extraction stage by disentangling it into two phases, namely neighbor sampling and subgraph gathering, which are respectively performed on CPU and GPU in an asynchronous fashion. The design favorably eliminates the dependence of feature learning on subgraph extraction, and is capable of exploiting the GPU's batch processing ability to remarkably boost computations throughout the pipeline. Dedicated data structures are designed for efficiently managing the dynamic graph storage and conforming efficient subgraph operations. Extensive empirical results on various real-world dynamic graphs show that GENTI achieves up to 30× faster in subgraph extraction time than the state-of-the-art walk-based methods and up to 26× acceleration in overall learning time, while maintaining comparable prediction performance. In particular, it is able to complete learning on the largest available graph of 1.3 billion edges within 24 hours, while all other baselines exhibit prohibitive overhead.

Location search in spatial services such as online maps nowadays is usually based on criteria filtering methods. Due to its algorithmic design, the traditional method only searches for one target location per query, ignoring the relation among multiple objects. In large-scale tasks of finding a number of targets, it is particularly inconvenient as users are required to specify each criterion. To address this issue, we present Example Searcher, a system that allows users to search more efficiently in such tasks. Example Searcher adopts a novel way of location search that allows rich and interactive user inputs and searches for multiple target locations simultaneously. The system is powered by the state-of-the-art spatial search algorithm LORA, which efficiently performs spatial queries and searches for a set of locations that are similar to the given input examples as a whole. An introduction video of Example Searcher is available at: https://sites.google.com/view/examplesearcher.

Subgraphs are obtained by extracting a subset of vertices and a subset of edges from the associated original graphs, and many graph properties are known to be inherited by subgraphs. Subgraphs can be applied in many areas such as social networks, recommender systems, biochemistry and fraud discovery. Researchers from various communities have paid a great deal of attention to investigate numerous subgraph problems, by proposing algorithms that mainly extract important structures of a given graph. There are however some limitations that should be addressed, with regard to the efficiency, effectiveness and scalability of these traditional algorithms. As a consequence, machine learning techniques---one of the most latest trends---have recently been employed in the database community to address various subgraph problems considering that they have been shown to be beneficial in dealing with graph-related problems. We discuss learning-based approaches for four well known subgraph problems in this tutorial, namely subgraph isomorphism, maximum common subgraph, community detection and community search problems. We give a general description of each proposed model, and analyse its design and performance. To allow further investigations on relevant subgraph problems, we suggest some potential future directions in this area. We believe that this work can be used as one of the primary resources, for researchers who intend to develop learning models in solving problems that are closely related to subgraphs.

Recent advances in data processing have stimulated the demand for learning graphs of very large scales. Graph Neural Networks (GNNs), being an emerging and powerful approach in solving graph learning tasks, are known to be difficult to scale up. Most scalable models apply node-based techniques in simplifying the expensive graph message-passing propagation procedure of GNN. However, we find such acceleration insufficient when applied to million- or even billion-scale graphs. In this work, we propose SCARA, a scalable GNN with feature-oriented optimization for graph computation. SCARA efficiently computes graph embedding from node features, and further selects and reuses feature computation results to reduce overhead. Theoretical analysis indicates that our model achieves sub-linear time complexity with a guaranteed precision in propagation process as well as GNN training and inference. We conduct extensive experiments on various datasets to evaluate the efficacy and efficiency of SCARA. Performance comparison with baselines shows that SCARA can reach up to 100x graph propagation acceleration than current state-of-the-art methods with fast convergence and comparable accuracy. Most notably, it is efficient to process precomputation on the largest available billion-scale GNN dataset Papers100M (111M nodes, 1.6B edges) in 100 seconds.