1 code implementation • 10 Aug 2024 • Yiran Li, Gongyao Guo, Jieming Shi, Renchi Yang, Shiqi Shen, Qing Li, Jun Luo

In this paper, we first present AHCKA as an efficient approach to attributed hypergraph clustering (AHC).

1 code implementation • 20 May 2024 • Renchi Yang, Yidu Wu, Xiaoyang Lin, Qichen Wang, Tsz Nam Chan, Jieming Shi

The severity of these issues is accentuated in real ABGs, which often encompass millions of nodes and a sheer volume of attribute data, rendering effective k-ABGC over such graphs highly challenging.

1 code implementation • 3 May 2024 • Ziang Zhou, Jieming Shi, Renchi Yang, Yuanhang Zou, Qing Li

We identify a potential semantic mixing issue in existing message passing processes, where the representations of the neighbors of a node $v$ are forced to be transformed to the feature space of $v$ for aggregation, though the neighbors are in different types.

1 code implementation • 28 Dec 2023 • Renchi Yang, Jieming Shi

A bipartite graph contains inter-set edges between two disjoint vertex sets, and is widely used to model real-world data, such as user-item purchase records, author-article publications, and biological interactions between drugs and proteins.

1 code implementation • 16 Oct 2023 • Zhihao Ding, Jieming Shi, Shiqi Shen, Xuequn Shang, Jiannong Cao, Zhipeng Wang, Zhi Gong

We find that substructure differences commonly exist between ID and OOD graphs, and design SGOOD with a series of techniques to encode task-agnostic substructures for effective OOD detection.

1 code implementation • 4 Sep 2023 • Zhihao Ding, Jieming Shi, Qing Li, Jiannong Cao

For example, on a Bitcoin dataset with 20 million nodes and 203 million edges, DIAM attains an F1 score of 96. 55%, markedly surpassing the runner-up's score of 83. 92%.

1 code implementation • 5 Aug 2023 • Zequan Xu, Qihang Sun, Shaofeng Hu, Jieming Shi, Hui Li

The rise of the click farm business using Multi-purpose Messaging Mobile Apps (MMMAs) tempts cybercriminals to perpetrate crowdsourcing frauds that cause financial losses to click farm workers.

no code implementations • 28 Jul 2023 • Jie Liu, Mengting He, Xuequn Shang, Jieming Shi, Bin Cui, Hongzhi Yin

By swapping the context embeddings between nodes and edges and measuring the agreement in the embedding space, we enable the mutual detection of node and edge anomalies.

1 code implementation • 12 Nov 2022 • Qianru Zhang, Zheng Wang, Cheng Long, Chao Huang, Siu-Ming Yiu, Yiding Liu, Gao Cong, Jieming Shi

Detecting anomalous trajectories has become an important task in many location-based applications.

no code implementations • 29 Sep 2021 • Ziang Zhou, Jieming Shi, Shengzhong Zhang, Zengfeng Huang, Qing Li

Therefore, we propose an effective framework, Stabilized self-training with Negative sampling (SN), which is applicable to existing GNNs to stabilize the training process and enhance the training data, and consequently, boost classification accuracy on graphs with few labeled data.

no code implementations • 7 Feb 2021 • Renchi Yang, Jieming Shi, Yin Yang, Keke Huang, Shiqi Zhang, Xiaokui Xiao

Given a graph G where each node is associated with a set of attributes, and a parameter k specifying the number of output clusters, k-attributed graph clustering (k-AGC) groups nodes in G into k disjoint clusters, such that nodes within the same cluster share similar topological and attribute characteristics, while those in different clusters are dissimilar.

no code implementations • 3 Mar 2020 • Tianyuan Jin, Pan Xu, Jieming Shi, Xiaokui Xiao, Quanquan Gu

Thompson sampling is one of the most widely used algorithms for many online decision problems, due to its simplicity in implementation and superior empirical performance over other state-of-the-art methods.

no code implementations • 19 Feb 2020 • Jieming Shi, Tianyuan Jin, Renchi Yang, Xiaokui Xiao, Yin Yang

Given a graph G and a node u in G, a single source SimRank query evaluates the similarity between u and every node v in G. Existing approaches to single source SimRank computation incur either long query response time, or expensive pre-computation, which needs to be performed again whenever the graph G changes.

1 code implementation • NeurIPS 2019 • Tianyuan Jin, Jieming Shi, Xiaokui Xiao, Enhong Chen

For PAC problem, we achieve optimal query complexity and use only $O(\log_{\frac{k}{\delta}}^*(n))$ rounds, which matches the lower bound of round complexity, while most of existing works need $\Theta(\log \frac{n}{k})$ rounds.

1 code implementation • 7 Oct 2019 • Ziang Zhou, Jieming Shi, Shengzhong Zhang, Zengfeng Huang, Qing Li

However, under extreme cases when very few labels are available (e. g., 1 labeled node per class), GNNs suffer from severe performance degradation.

no code implementations • 17 Jun 2019 • Renchi Yang, Jieming Shi, Xiaokui Xiao, Yin Yang, Sourav S. Bhowmick

Given an input graph G and a node v in G, homogeneous network embedding (HNE) maps the graph structure in the vicinity of v to a compact, fixed-dimensional feature vector.

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