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Works162 from public data
- Graph Representation Learning via Graphical Mutual Information Maximization706
An unsupervised learning model trained by maximizing GMI between the input and output of a graph neural encoder is developed, which outperforms state-of-the-art unsuper supervised counterparts, and even sometimes exceeds the performance of supervised ones.
- 332
- ANOMALOUS: A Joint Modeling Approach for Anomaly Detection on Attributed Networks264
This paper proposes a novel joint framework to conduct attribute selection and anomaly detection as a whole based on CUR decomposition and residual analysis and results on both synthetic and real-world datasets corroborate the effectiveness of the proposed framework.
- TwiBot-20: A Comprehensive Twitter Bot Detection Benchmark249
TwiBot-20 is a massive Twitter bot detection benchmark, which contains 229,573 users, 33,488,192 tweets, 8,723,736 user property items and 455,958 follow relationships, and is considered to be the largest Twitterbot detection benchmark to date.
- 223
- TwiBot-22: Towards Graph-Based Twitter Bot Detection198
TwiBot-22 is proposed, a comprehensive graph-based Twitter bot detection benchmark that presents the largest dataset to date, provides diversified entities and relations on the Twitter network, and has considerably better annotation quality than existing datasets.
- BotRGCN196
The proposed BotRGCN, which is short for Bot detection with Relational Graph Convolutional Networks, addresses the challenge of community by constructing a heterogeneous graph from follow relationships and applies relational graph convolutional networks.
- Heterogeneity-Aware Twitter Bot Detection with Relational Graph Transformers167
A novel bot detection framework is proposed, which leverages the topological structure of user-formed heterogeneous graphs and models varying influence intensity between users, and proposes relational graph transformers to model heterogeneous influence between users and learn node representations.
- Self-weighted Robust LDA for Multiclass Classification with Edge Classes123
A novel self-weighted robust LDA with ℓ2,1-norm based pairwise between-class distance criterion, called SWRLDA, is proposed, which can automatically avoid the optimal mean calculation and simultaneously learn adaptive weights for each class pair without setting any additional parameter.
- Semantics-Guided Contrastive Network for Zero-Shot Object Detection115
A novel Semantics-Guided Contrastive Network for ZSD is developed, named ContrastZSD, a detection framework that first brings contrastive learning mechanism into the realm of zero-shot detection and outperforms the previous state-of-the-art on both ZSD and generalized ZSD tasks.
- SATAR109
SATAR is proposed, a self-supervised representation learning framework of Twitter users, and applied to the task of bot detection, which demonstrates that SATAR outperforms competitive baselines on different bot detection datasets of varying information completeness and collection time.
- 105
- Scalable attack on graph data by injecting vicious nodes103
This paper develops a more scalable framework named Approximate Fast Gradient Sign Method which considers a more practical attack scenario where adversaries can only inject new vicious nodes to the graph while having no control over the original graph.
- 91
- Graph Few-shot Learning with Attribute Matching84
The proposed AMM-GNN leverages an attribute-level attention mechanism to capture the distinct information of each task and thus learns more effective transferable knowledge for meta-learning.
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- BotMoE: Twitter Bot Detection with Community-Aware Mixtures of Modal-Specific Experts79
BotMoE is proposed, a Twitter bot detection framework that jointly utilizes multiple user information modalities (metadata, textual content, network structure) to improve the detection of deceptive bots and incorporates a community-aware Mixture-of-Experts (MoE) layer to improve domain generalization and adapt to different Twitter communities.
- Simple to complex cross-modal learning to rank79
This paper involves the self-paced learning theory with diversity into the cross-modal learning to rank and learns an optimal multi- modal embedding space based on non-linear mapping functions that enhances the model’s robustness to outliers and achieves better generalization via training the model gradually from easy rankings by diverse queries to more complex ones.
- A new self-supervised task on graphs: Geodesic distance prediction70
This paper introduces a novel self-supervised strategy for graph representation learning by exploiting natural supervision provided by the data itself by randomly selecting pairs of nodes in a graph and training a well-designed neural net to predict the contextual position of one node relative to the other.
- Noisy Correspondence Learning with Meta Similarity Correction69
A Meta Similarity Correction Network (MSCN) is proposed to provide reliable similarity scores and an effective data purification strategy using meta-data as prior knowledge to remove the noisy samples.
- Event-Radar: Event-driven Multi-View Learning for Multimodal Fake News Detection68
An Event-Radar based on multi-view learning based on multi-view learning is proposed based on multi-view learning, which integrates visual manipulation, textual emotion and mul-014 timodal inconsistency at event-level for fake news detection.
- Semantics-Preserving Graph Propagation for Zero-Shot Object Detection68
This paper proposes a novel Semantics-Preserving Graph Propagation model for ZSD based on Graph Convolutional Networks (GCN), and develops a graph construction module to flexibly build category graphs by leveraging diverse correlations between category nodes, followed by two semantics-preserving graph propagation modules that enhance both category and region representations.
- Mitigating World Biases: A Multimodal Multi-View Debiasing Framework for Fake News Video Detection65
A Multimodal Multi-View Debiasing (MMVD) framework, which makes the first attempt to mitigate various multimodal biases for fake news video detection and put forward a multi-view causal reasoning strategy to learn unbiased dependencies within the cognitive biases, thus enhancing the unbiased prediction of multimodal videos.
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- KRACL: Contrastive Learning with Graph Context Modeling for Sparse Knowledge Graph Completion59
This work proposes a novel framework KRACL1 to alleviate the widespread sparsity in KGs with graph context and contrastive learning, and proposes the Knowledge Relational Attention Network (KRAT) to leverage the graph context by simultaneously projecting neighboring triples to different latent spaces and jointly aggregating messages with the attention mechanism.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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