Search Results for author:
Found 13 papers, 6 papers with code
Efficient High-Quality Clustering for Large Bipartite Graphs
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.
SGOOD: Substructure-enhanced Graph-Level Out-of-Distribution Detection
However, existing graph-level models are generally built on i. i. d.
Effective Multi-Graph Neural Networks for Illicit Account Detection on Cryptocurrency Transaction Networks
Extensive experiments, comparing against 14 existing solutions on 4 large cryptocurrency datasets of Bitcoin and Ethereum, demonstrate that DIAM consistently achieves the best performance to accurately detect illicit accounts, while being efficient.
Crowdsourcing Fraud Detection over Heterogeneous Temporal MMMA Graph
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.
BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection
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.
Online Anomalous Subtrajectory Detection on Road Networks with Deep Reinforcement Learning
Detecting anomalous trajectories has become an important task in many location-based applications.
Stabilized Self-training with Negative Sampling on Few-labeled Graph Data
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.
Effective and Scalable Clustering on Massive Attributed Graphs
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.
MOTS: Minimax Optimal Thompson Sampling
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.
Realtime Index-Free Single Source SimRank Processing on Web-Scale Graphs
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.
Efficient Pure Exploration in Adaptive Round model
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.
Effective Stabilized Self-Training on Few-Labeled Graph Data
However, under extreme cases when very few labels are available (e. g., 1 labeled node per class), GNNs suffer from severe performance degradation.
Homogeneous Network Embedding for Massive Graphs via Reweighted Personalized PageRank
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.
