18 accepted papers
Inductive knowledge graph completion (KGC) aims to predict missing links involving unseen entities, making it a particularly challenging task for knowledge representation learning. Traditional embedding-based methods often fall short in this setting due to their limited structural reasoning capabili
Representation learning on text-attributed graphs (TAGs) is crucial for real-world applications, as it enables effective modeling of both rich node semantics and complex graph structure. Nevertheless, this task is intrinsically challenging due to structural–semantic mismatch stemming from divergent …
Graph Transformers (GTs) have emerged as a powerful paradigm for graph representation learning, leveraging attention mechanisms to enable flexible information exchange. Recent GTs often adopt local attention to restrict computation to neighborhoods, thereby reducing costs and enhancing scalability.
Deep attribute graph clustering aims to learn discriminative node representations by leveraging both node attributes and graph topology to partition nodes into distinct clusters. Although substantial progress has been made in attribute-graph clustering in recent years, two key challenges remain: noi…
Session-based Recommendation (SBR) aims to predict users’ next interaction based on their current session without relying on long-term profiles. Despite its effectiveness in privacy-preserving and real-time scenarios, SBR remains challenging due to limited behavioral signals. Prior methods often ove
Retrieving entity knowledge that aligns with user intent is essential for task-oriented dialogue (TOD) systems to support personalization and localization, especially under large-scale knowledge bases. However, generative models tend to suffer from implicit association preference, while retrieval-ge
Fraud detection that aims to discern frauds from the majority of benigns has become an increasingly prominent research field. Recently, Graph Neural Networks (GNNs) have been widely applied in graph-based fraud detection due to their outstanding data analysis and mining capabilities. However, owing…
The cold-start recommendation has been challenging due to the limited historical interactions for new users and new items. Recently, methods based on meta-learning and graph neural networks have been effective in this problem. However, these methods mainly focus on the missing user-item interactions…
Graph Neural Networks (GNNs) have recently achieved significant success in several graph-related tasks. However, traditional GNNs and their variants are constantly limited by the implicit homophily, assuming neighboring nodes belong to the same class. This results in weak performance on heterophilic…
Heterophily emerges as a critical challenge in Graph Anomaly Detection (GAD). Recent studies reveal that neighborhood distributions, rather than heterophily itself, are the fundamental factor for the expressive power of Graph Neural Networks (GNNs). However, two key challenges remain unresolved. Fir
Knowledge Graphs (KGs) are structured data presented as directed graphs. Due to the common issues of incompleteness and inaccuracy encountered during construction and maintenance, completing KGs becomes a critical task. Inductive Knowledge Graph Completion (KGC) excels at inferring patterns or model…
Graph Neural Networks (GNNs), a type of neural network that can learn from graph-structured data through neighborhood information aggregation, have shown superior performance in various downstream tasks. However, as the number of layers increases, node representations becomes indistinguishable, whic…
Graph neural networks have been demonstrated to be effective analytic apparatus for mining network data. Most real-world networks are inherently hierarchical, offering unique opportunities to acquire latent, intrinsic network organizational properties by utilizing network taxonomies. The existing ap…
Graph Neural Networks (GNNs) have received widespread attention and applications due to their excellent performance in graph representation learning. Most existing GNNs can only aggregate 1-hop neighbors in a GNN layer, so they usually stack multiple GNN layers to obtain more information from larger…
In the realm of cross-network tasks, graph domain adaptation is an effective tool due to its ability to transfer abundant labels from nodes in the source domain to those in the target domain. Existing adversarial domain adaptation methods mainly focus on domain-wise alignment. These approaches, whil…
Most Graph Neural Networks (GNNs) are based on the homophily assumption, where nodes with the same labels or similar features tend to be connected to each other. However, real-world graphs often do not adhere to this homophily assumption. Currently, most researches aggregate multi-hop neighbor infor…
Graph Convolutional Networks (GCNs), aiming to obtain the representation of a node by aggregating its neighbors, have demonstrated great power in tackling various analytics tasks on graph (network) data. The remarkable performance of GCNs typically relies on the homophily assumption of networks, whi…
Community detection, aiming at partitioning a network into multiple substructures, is practically importance. Graph convolutional network (GCN), a new deep-learning technique, has recently been developed for community detection. Markov Random Fields (MRF) has been combined with GCN in the MRFasGCN m…