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Found 16 papers, 10 papers with code
A Versatile Framework for Attributed Network Clustering via K-Nearest Neighbor Augmentation
In this paper, we first present AHCKA as an efficient approach to attributed hypergraph clustering (AHC).
Effective Clustering on Large Attributed Bipartite Graphs
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.
SlotGAT: Slot-based Message Passing for Heterogeneous Graph Neural Network
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.
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
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.
Effective Illicit Account Detection on Large Cryptocurrency MultiGraphs
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%.
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.
