Paper-Conference |

Mining Discriminative Salient Objects with Optimal Transport for Few-Shot Image Classification

Wed, 08 Jul 2026 00:00:00 +0000

FLINS-ISKE, 2026

AEFA: An Ensemble Framework for Fraud Detection in the Forex Market

Sat, 18 Oct 2025 00:00:00 +0000

International Conference on Advanced Data Mining and Applications, 2025

Deep Overlapping Community Search via Subspace Embedding

Mon, 10 Feb 2025 00:00:00 +0000

ACM SIGMOD Conference, 2025

Overlapping Community Search (OCS) identifies nodes that interact with multiple communities based on a specified query. Existing community search approaches fall into two categories: algorithm-based models and Machine Learning-based (ML) models. Despite the long-standing focus on this topic within the database domain, current solutions face two major limitations: 1) Both approaches fail to address personalized user requirements in OCS, consistently returning the same set of nodes for a given query regardless of user differences. 2) Existing ML-based CS models suffer from severe training efficiency issues. In this paper, we formally redefine the problem of OCS. By analyzing the gaps in both types of approaches, we then propose a general solution for OCS named S parse S ubspace F ilter (SSF), which can extend any ML-based CS model to enable personalized search in overlapping structures. To overcome the efficiency issue in the current models, we introduce S implified M ulti-hop Attention N etworks (SMN), a lightweight yet effective community search model with larger receptive fields. To the best of our knowledge, this is the first ML-based study of overlapping community search. Extensive experiments validate the superior performance of SMN within the SSF pipeline, achieving a 13.73% improvement in F1-Score and up to 3 orders of magnitude acceleration in model efficiency compared to state-of-the-art approaches.

RPDN: An Effective Rating Pollution Attacks Detection Framework for Recommendation Systems

Wed, 01 Jan 2025 00:00:00 +0000

Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2025

Neural Similarity Search on Supergraph Containment (Extended Abstract)

Mon, 13 May 2024 00:00:00 +0000

IEEE International Conference on Data Engineering, 2024

Supergraph search is a fundamental graph query processing problem. Supergraph search aims to find all data graphs contained in a given query graph based on the subgraph isomorphism. In other words, the goal is to determine if part of the query graph is the same as a smaller data graph. Existing algorithms construct the indices and adopt the filtering-and-verification framework, which is usually computationally expensive and can cause redundant computations. Recently, various learning-based methods have been proposed for a good trade-off between accuracy and efficiency for query processing tasks. However, to our knowledge, no learning-based method is proposed for the supergraph search task. In this paper, we propose the first learning-based method for similarity search on supergraph containment, named Neural Supergraph similarity Search (NSS). NSS first learns the representations for query and data graphs and then efficiently conducts the supergraph search on the representation space, the complexity of which is linear to the number of data graphs. The carefully designed Wasserstein discriminator and reconstruction network enable NSS to capture better the interrelation, structural and label information between and within the query and data graphs. Experiments demonstrate that the NSS is up to 6 orders of magnitude faster than the state-of-the-art exact supergraph search algorithm in query processing and is more accurate than the other learning-based solutions.

SBGMN: A Multi-view Sign Prediction Network for Bipartite Graphs

Mon, 01 Jan 2024 00:00:00 +0000

Asia Pacific Web Conference (APWeb-WAIM), 2024

KGNIE: A Learning Method for Estimating Node Importance in Knowledge Graphs

Sun, 17 Dec 2023 00:00:00 +0000

IEEE International Conference on High Performance Computing and Communications, 2023

Estimating node importance is critical in graph data mining, benefiting various downstream applications such as social network analysis and recommendation systems. Existing approaches face challenges when dealing with complex knowledge graphs due to the abundance of predicate and entity information. This paper introduces KGNIE, an efficient knowledge graph node importance estimation network. KGNIE considers the rich predicate attributes and entity types, and utilizes local and global information encoders to generate node embeddings with different importance-related information. An attention-based fusion module is employed to balance the two encoders. A node importance decoder is proposed to map node embed dings to importance scores based on entity types. Furthermore, we introduce a margin ranking loss to determine relative node importance rankings. We conducted extensive experiments on real-world knowledge graphs, demonstrating that our model outperforms existing approaches across all evaluation metrics.

IFGNN: An Individual Fairness Awareness Model for Missing Sensitive Information Graphs

Mon, 06 Nov 2023 00:00:00 +0000

Australasian Database Conference, 2023

Group-based Fraud Detection Network on e-Commerce Platforms

Fri, 04 Aug 2023 00:00:00 +0000

ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Along with the rapid technological and commercial innovation on the e-commerce platforms, there are an increasing number of frauds that bring great harm to these platforms. Many frauds are conducted by organized groups of fraudsters for higher efficiency and lower costs, which are also known as group-based frauds. Despite the high concealment and strong destructiveness of group-based fraud, there is no existing research work that can thoroughly exploit the information within the transaction networks of e-commerce platforms for group-based fraud detection. In this work, we analyze and summarize the characteristics of group-based frauds, based on which we propose a novel end-to-end semi-supervised Group-based Fraud Detection Network (GFDN) to support such fraud detection in real-world applications. Experimental results on large-scale e-commerce datasets from Taobao and Bitcoin trading datasets show the superior effectiveness and efficiency of our proposed model for group-based fraud detection on bipartite graphs.