Credit risk assessment has increasingly become a prominent research field due to the dramatically increased incidents of financial default. Traditional graph-based methods have been developed to detect defaulters within user-merchant commercial payment networks. However, these methods face challenges in detecting complex risks, primarily due to their neglect of user-to-user fund transfer interactions and the under-utilization of temporal information. In this paper, we propose a novel framework named Dynamic Graph Neural Network with Static Relations (DGNN-SR) for credit risk assessment, which can encode the dynamic transaction graph and the static fund transfer graph simultaneously. To fully harness the temporal information, DGNN-SR employs a multi-view time encoder to explore the semantics of both relative and absolute time. To enhance the dynamic representations with static relations, we devise an adaptive re-weighting strategy to incorporate the static relations into the dynamic representations of time encoder, which extracts more discriminative features for risk assessment. Extensive experiments on two real-world business datasets demonstrate that our proposed method achieves a 0.85% - 2.5% improvement over existing SOTA methods.

Subgraph matching is one of the fundamental problems in graph analytics. Existing methods generate matching orders to guide their search, which consists of a series of extensions. Each time, they extend smaller partial matches into larger ones until all complete answers are obtained. However, these methods have two significant drawbacks. Firstly, their matching order generations are usually heuristic and challenging to be effective for different queries. Secondly, each extension, serving as its computation unit, is coarse-grained and may hinder performance. This granularity issue stems from merging generation and expansion operations into a single computation unit. To address these challenges, we introduce a pioneering framework for Reinforced Subgraph Matching (RSM) that features a fine-grained operation-based search plan. Initially, RSM proposes a fresh paradigm for search, referred to as operation-level search, where each computation unit is defined as an operation that either generates or expands a candidate set under a query vertex. To deal with the second problem and fully exploit the potential of this novel search paradigm, RSM implements a reinforcement learning strategy to generate operation-level search plans. RSM's reinforcement learning approach for constructing operation-based search plans encompasses three modules. In the first module, we employ graph neural networks to extract query vertex representation from graphs. Then, the other two modules leverage multilayer perceptron and are designed to create the generation and expansion operations, respectively. Extensive experiments on real-world graph datasets validate that RSM cuts down query processing time, outperforming existing algorithms by up to 1 to 2 orders of magnitude.

In this study, we address the problem of unnecessary computations in traditional continuous subgraph matching (CSM) frameworks due to premature expansions of the search space in dynamic graphs. Traditional CSM frameworks expand small partial matches according to a specific matching order until the final results are obtained. This extension involves two sequential steps: computing candidate vertices for an unmapped query vertex and expanding the search space using these candidate data. However, this long-established search model has a potential flaw, as premature expansions of the search space can lead to unnecessary computations. To address this issue, we introduce a novel search process, NewSP. Unlike traditional methods, NewSP emphasizes operations rather than extensions, incorporating a unique feature of postponing expansion at the operation level. This approach prevents premature expansions without compromising the initial pruning power of the selected matching order. Furthermore, NewSP allows for multiple consecutive expansions, paving the way for a multi-expansion strategy for further optimization. Our model also enables the implementation of cache strategies for candidate set reuse, as it does not necessitate immediate expansion of a candidate set once identified. To improve performance, we propose an adaptive index filtering strategy independent of the specific index used. Comprehensive experiments demonstrate that our method improves by up to two to three orders of magnitude compared to traditional algorithms. A case study showed that NewSP can accelerate the majority of subgraph matching algorithms.

Tourism has become the world's largest economy industry. More and more people share their travelogues on travel websites. Recommender system is an effective tool to provide travel services (e.g., Landscapes selection) for tourists. Many recommender systems are based on travel data that are supplied by travel agencies, and provide travel packages from a fixed package set, which bring two challenges for travel package recommender system. One is how to generate more travel packages. The other is how to measure more fine-grained user similarity. To address these challenges, we develop a package generation and recommendation framework to help travelers select landscapes. Firstly, we propose a Fuzzy Clustering based Package Generation algorithm (FCPG) to generate new travel packages to improve the overall recommendation effectiveness. Then, we develop a Dual Topic Model based Package Recommendation algorithm (DTMPR). It considers two user-related topics (travel seasons and areas), and provides more fine-grained user similarity measure. Experimental results show the superiority of our framework in comparison with the state-of-the-art methods.