Predicting traffic speed is a crucial task in intelligent transportation systems, as it helps analyze traffic congestion and improve road flow. The complex spatio-temporal interactions present in traffic data make accurate predictions challenging. In recent years, many studies have focused on extracting and learning spatio-temporal features. Deep learning methods, particularly spatio-temporal graph learning models, show promising performance and become the mainstream approach in this area of research. However, existing methods cannot exploit and unify the heterogeneous spatio-temporal interactions hidden in traffic data to achieve multi-correlation modeling. As a result, they cannot effectively model the complex evolving patterns in traffic dynamics. To this end, we propose a heterogeneous spatio-temporal traffic graph learning framework (HSTGL) to capture these diverse spatio-temporal interactions comprehensively. In terms of design, HSTGL consists of three modules: spatio-temporal heterogeneous graph construction, spatio-temporal heterogeneous graph attention learning, and heterogeneous information supplementation. The first two modules utilize similar temporal pattern clustering and heterogeneous spatio-temporal graph attention mechanisms (HSTGAT) to learn heterogeneous spatio-temporal interactions in traffic data. The latter feature fusion module (FFM) is developed to complement potential heterogeneous information in the global spatio-temporal context. Our HSTGL conducts extensive experiments on three real-world public traffic datasets: METR-LA, PEMS-BAY, and PEMSD7M. The results demonstrate that HSTGL achieves superior predictive performance compared to representative benchmark methods with average improvements of 3.3% in MAE, 1.8% in RMSE, and 5.2% in MAPE compared to the optimal baseline.

Urban region representation is crucial for various urban downstream tasks. However, despite the proliferation of methods and their success, acquiring general urban region knowledge and adapting to different tasks remains challenging. Existing work pays limited attention to the fine-grained functional layout semantics in urban regions, limiting their ability to capture transferable knowledge across regions. Further, inadequate handling of the unique features and relationships required for different downstream tasks may also hinder effective task adaptation. In this paper, we propose a Graph-based Urban Region Pre-training and Prompting framework (GURPP) for region representation learning. Specifically, we first construct an urban region graph and develop a subgraph-centric urban region pre-training model to capture the heterogeneous and transferable patterns of entity interactions. This model pre-trains knowledge-rich region embeddings using contrastive learning and multi-view learning methods. To further refine these representations, we design two graph-based prompting methods: a manually-defined prompt to incorporate explicit task knowledge and a task-learnable prompt to discover hidden knowledge, which enhances the adaptability of these embeddings to different tasks. Extensive experiments on various urban region prediction tasks and different cities demonstrate the superior performance of our framework.

This paper studies cost-effective graph cleaning with a single machine. We adopt a rule-based method that may embed machine learning models as predicates in the rules. Graph cleaning with the rules involves rule discovery, error detection and correction. These tasks are both computation-heavy and I/O-intensive as they repeatedly invoke costly graph pattern matching, and produce a large amount of a large volume of intermediate results, among other things. In light of these, no existing single-machine system is able to carry out these tasks even on not-too-large graphs, even using GPUs. Thus we develop MiniClean, a single-machine system for cleaning large graphs. It proposes (1) a workflow that better fits a single machine by pipelining CPU, GPU and I/O operations; (2) memory footprint reduction with bundled processing and data compression; and (3) a multi-mode parallel model for SIMD, pipelined and independent parallelism, and their scheduling to maximize CPU--GPU synergy. Using real-life graphs, we empirically verify that MiniClean outperforms the SOTA single-machine systems by at least 65.34× and multi-machine systems with 32 nodes by at least 8.09×.

Matching tabular data to a knowledge graph (KG) is critical for understanding the semantic column types, column relationships, and entities of a table. Existing matching approaches rely heavily on core columns that represent primary subject entities on which other columns in the table depend. However, discovering these core columns before understanding the table’s semantics is challenging. Most prior works use heuristic rules, such as the leftmost column, to discover a single core column, while an insightful discovery of the core column set that accurately captures the dependencies between columns is often overlooked. To address these challenges, we introduce Dependency-aware Core Column Set Discovery ( DaCo ), an iterative method that uses a novel rough matching strategy to identify both inter-column dependencies and the core column set. Additionally, DaCo can be seamlessly integrated with pre-trained language models, as proposed in the optimization module. Unlike other methods, DaCo does not require labeled data or contextual information, making it suitable for real-world scenarios. In addition, it can identify multiple core columns within a table, which is common in real-world tables. We conduct experiments on six datasets, including five datasets with single core columns and one dataset with multiple core columns. Our experimental results show that DaCo outperforms existing core column set detection methods, further improving the effectiveness of table understanding tasks.

Node classification is a substantial problem in graph-based fraud detection. Many existing works adopt Graph Neural Networks (GNNs) to enhance fraud detectors. While promising, currently most GNN-based fraud detectors fail to generalize to the low homophily setting. Besides, label utilization has been proved to be significant factor for node classification problem. But we find they are less effective in fraud detection tasks due to the low homophily in graphs. In this work, we propose GAGA, a novel Group AGgregation enhanced TrAnsformer, to tackle the above challenges. Specifically, the group aggregation provides a portable method to cope with the low homophily issue. Such an aggregation explicitly integrates the label information to generate distinguishable neighborhood information. Along with group aggregation, an attempt towards end-to-end trainable group encoding is proposed which augments the original feature space with the class labels. Meanwhile, we devise two additional learnable encodings to recognize the structural and relational context. Then, we combine the group aggregation and the learnable encodings into a Transformer encoder to capture the semantic information. Experimental results clearly show that GAGA outperforms other competitive graph-based fraud detectors by up to 24.39% on two trending public datasets and a real-world industrial dataset from Baidu. Even more, the group aggregation is demonstrated to outperform other label utilization methods (e.g., C&S, BoT/UniMP) in the low homophily setting.

A knowledge graph (KG) manages large-scale and real-world facts as a big graph in a schema-flexible manner. Aggregate query is a fundamental query over KGs, e.g., “what is the average price of cars produced in Germany?”. Despite its importance, answering aggregate queries on KGs has received little attention in the literature. Aggregate queries can be supported based on factoid queries, e.g., “find all cars produced in Germany”, by applying an additional aggregate operation on factoid queries' answers. However, this straightforward method is challenging because both the accuracy and efficiency of factoid query processing will seriously impact the performance of aggregate queries. In this paper, we propose a “sampling-estimation” model to answer aggregate queries over KGs, which is the first work to provide an approximate aggregate result with an effective accuracy guarantee, and without relying on factoid queries. Specifically, we first present a semantic-aware sampling to collect a high-quality random sample through a random walk based on knowledge graph embedding. Then, we propose unbiased estimators for COUNT, SUM, and a consistent estimator for AVG to compute the approximate aggregate results based on the random sample, with an accuracy guarantee in the form of confidence interval. We extend our approach to support iterative improvement of accuracy, and more complex queries with filter, GROUP-BY, and different graph shapes, e.g., chain, cycle, star, flower. Extensive experiments over real-world KGs demonstrate the effectiveness and efficiency of our approach.

Recently, graph query is widely adopted for querying knowledge graphs. Given a query graph GQ, the graph query finds subgraphs in a knowledge graph G that exactly or approximately match GQ. We face two challenges on graph query: (1) the structural gap between GQand the predefined schema in G causes mismatch with query graph, (2) users cannot view the answers until the graph query terminates, leading to a longer system response time (SRT). In this paper, we propose a semantic-guided and response-time-bounded graph query to return the top-k answers effectively and efficiently. We leverage a knowledge graph embedding model to build the semantic graph SGQ, and we define the path semantic similarity (pss) over SGQas the metric to evaluate the answer's quality. Then, we propose an A* semantic search on SGQto find the top-k answers with the greatest pss via a heuristic pss estimation. Furthermore, we make an approximate optimization on A* semantic search to allow users to trade off the effectiveness for SRT within a user- specific time bound. Extensive experiments over real datasets confirm the effectiveness and efficiency of our solution.

Web-scale information networks containing billions of entities are common nowadays. Querying these networks can be modeled as a subgraph matching problem. Since information networks are incomplete and noisy in nature, it is important to discover answers that match exactly as well as answers that are similar to queries. Existing graph matching algorithms usually use graph indices to improve the efficiency of query processing. For web-scale information networks, it may not be feasible to build the graph indices due to the amount of work and the memory/storage required. In this paper, we propose an efficient algorithm for finding the best k answers for a given query without precomputing graph indices. The quality of an answer is measured by a matching score that is computed online. To speed up query processing, we propose a novel technique for bounding the matching scores during the computation. By using bounds, we can efficiently prune the answers that have low qualities without having to evaluate all possible answers. The bounding technique can be implemented in a distributed environment, allowing our approach to efficiently answer the queries on web-scale information networks. We demonstrate the effectiveness and the efficiency of our approach through a series of experiments on real-world information networks. The result shows that our bounding technique can reduce the running time up to two orders of magnitude comparing to an approach that does not use bounds.