Twibot-22: Towards graph-based twitter bot detection
Twitter bot detection has become an increasingly important task to combat misinformation,
facilitate social media moderation, and preserve the integrity of the online discourse. State-of …
facilitate social media moderation, and preserve the integrity of the online discourse. State-of …
Simple and efficient heterogeneous graph neural network
Heterogeneous graph neural networks (HGNNs) have the powerful capability to embed rich
structural and semantic information of a heterogeneous graph into node representations …
structural and semantic information of a heterogeneous graph into node representations …
Learning latent relations for temporal knowledge graph reasoning
Abstract Temporal Knowledge Graph (TKG) reasoning aims to predict future facts based on
historical data. However, due to the limitations in construction tools and data sources, many …
historical data. However, due to the limitations in construction tools and data sources, many …
A survey on graph representation learning methods
S Khoshraftar, A An - ACM Transactions on Intelligent Systems and …, 2024 - dl.acm.org
Graph representation learning has been a very active research area in recent years. The
goal of graph representation learning is to generate graph representation vectors that …
goal of graph representation learning is to generate graph representation vectors that …
Knowledge-adaptive contrastive learning for recommendation
By jointly modeling user-item interactions and knowledge graph (KG) information, KG-based
recommender systems have shown their superiority in alleviating data sparsity and cold start …
recommender systems have shown their superiority in alleviating data sparsity and cold start …
Hgprompt: Bridging homogeneous and heterogeneous graphs for few-shot prompt learning
Graph neural networks (GNNs) and heterogeneous graph neural networks (HGNNs) are
prominent techniques for homogeneous and heterogeneous graph representation learning …
prominent techniques for homogeneous and heterogeneous graph representation learning …
Learning multi-granularity consecutive user intent unit for session-based recommendation
Session-based recommendation aims to predict a user's next action based on previous
actions in the current session. The major challenge is to capture authentic and complete …
actions in the current session. The major challenge is to capture authentic and complete …
Hinormer: Representation learning on heterogeneous information networks with graph transformer
Recent studies have highlighted the limitations of message-passing based graph neural
networks (GNNs), eg, limited model expressiveness, over-smoothing, over-squashing, etc …
networks (GNNs), eg, limited model expressiveness, over-smoothing, over-squashing, etc …
Refactor gnns: Revisiting factorisation-based models from a message-passing perspective
Abstract Factorisation-based Models (FMs), such as DistMult, have enjoyed enduring
success for Knowledge Graph Completion (KGC) tasks, often outperforming Graph Neural …
success for Knowledge Graph Completion (KGC) tasks, often outperforming Graph Neural …
The heterophilic graph learning handbook: Benchmarks, models, theoretical analysis, applications and challenges
Homophily principle,\ie {} nodes with the same labels or similar attributes are more likely to
be connected, has been commonly believed to be the main reason for the superiority of …
be connected, has been commonly believed to be the main reason for the superiority of …