DELTA: Dual Consistency Delving with Topological Uncertainty for Active Graph Domain Adaptation

• URL: http://arxiv.org/abs/2409.08946v2
• Date: Thu, 15 May 2025 03:38:43 GMT
• Title: DELTA: Dual Consistency Delving with Topological Uncertainty for Active Graph Domain Adaptation
• Authors: Pengyun Wang, Yadi Cao, Chris Russell, Yanxin Shen, Junyu Luo, Ming Zhang, Siyu Heng, Xiao Luo,
• Abstract summary: We propose a novel approach named Dual Consistency Delving with Topological Uncertainty (DELTA) for active graph domain adaptation.<n>Our DELTA consists of an edge-oriented graph subnetwork and a path-oriented graph subnetwork, which can explore topological semantics from complementary perspectives.<n>Experiments on benchmark datasets demonstrate that DELTA outperforms various state-of-the-art approaches.
• Score: 16.66618682610917
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph domain adaptation has recently enabled knowledge transfer across different graphs. However, without the semantic information on target graphs, the performance on target graphs is still far from satisfactory. To address the issue, we study the problem of active graph domain adaptation, which selects a small quantitative of informative nodes on the target graph for extra annotation. This problem is highly challenging due to the complicated topological relationships and the distribution discrepancy across graphs. In this paper, we propose a novel approach named Dual Consistency Delving with Topological Uncertainty (DELTA) for active graph domain adaptation. Our DELTA consists of an edge-oriented graph subnetwork and a path-oriented graph subnetwork, which can explore topological semantics from complementary perspectives. In particular, our edge-oriented graph subnetwork utilizes the message passing mechanism to learn neighborhood information, while our path-oriented graph subnetwork explores high-order relationships from sub-structures. To jointly learn from two subnetworks, we roughly select informative candidate nodes with the consideration of consistency across two subnetworks. Then, we aggregate local semantics from its K-hop subgraph based on node degrees for topological uncertainty estimation. To overcome potential distribution shifts, we compare target nodes and their corresponding source nodes for discrepancy scores as an additional component for fine selection. Extensive experiments on benchmark datasets demonstrate that DELTA outperforms various state-of-the-art approaches. The code implementation of DELTA is available at https://github.com/goose315/DELTA.

Related papers

• Hypergraph-enhanced Dual Semi-supervised Graph Classification [14.339207883093204]
  We propose a Hypergraph-Enhanced DuAL framework named HEAL for semi-supervised graph classification. To better explore the higher-order relationships among nodes, we design a hypergraph structure learning to adaptively learn complex node dependencies. Based on the learned hypergraph, we introduce a line graph to capture the interaction between hyperedges.
  arXiv  Detail & Related papers  (2024-05-08T02:44:13Z)
• DGNN: Decoupled Graph Neural Networks with Structural Consistency between Attribute and Graph Embedding Representations [62.04558318166396]
  Graph neural networks (GNNs) demonstrate a robust capability for representation learning on graphs with complex structures. A novel GNNs framework, dubbed Decoupled Graph Neural Networks (DGNN), is introduced to obtain a more comprehensive embedding representation of nodes. Experimental results conducted on several graph benchmark datasets verify DGNN's superiority in node classification task.
  arXiv  Detail & Related papers  (2024-01-28T06:43:13Z)
• Graph Transformer GANs with Graph Masked Modeling for Architectural Layout Generation [153.92387500677023]
  We present a novel graph Transformer generative adversarial network (GTGAN) to learn effective graph node relations. The proposed graph Transformer encoder combines graph convolutions and self-attentions in a Transformer to model both local and global interactions. We also propose a novel self-guided pre-training method for graph representation learning.
  arXiv  Detail & Related papers  (2024-01-15T14:36:38Z)
• Deep Manifold Graph Auto-Encoder for Attributed Graph Embedding [51.75091298017941]
  This paper proposes a novel Deep Manifold (Variational) Graph Auto-Encoder (DMVGAE/DMGAE) for attributed graph data. The proposed method surpasses state-of-the-art baseline algorithms by a significant margin on different downstream tasks across popular datasets.
  arXiv  Detail & Related papers  (2024-01-12T17:57:07Z)
• Domain Adaptive Graph Classification [0.0]
  We introduce the Dual Adversarial Graph Representation Learning (DAGRL), which explore the graph topology from dual branches and mitigate domain discrepancies via dual adversarial learning. Our approach incorporates adaptive perturbations into the dual branches, which align the source and target distribution to address domain discrepancies.
  arXiv  Detail & Related papers  (2023-12-21T02:37:56Z)
• BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection [50.26074811655596]
  We propose a novel unified graph anomaly detection framework based on bootstrapped self-supervised learning (named BOURNE) By swapping the context embeddings between nodes and edges, we enable the mutual detection of node and edge anomalies. BOURNE can eliminate the need for negative sampling, thereby enhancing its efficiency in handling large graphs.
  arXiv  Detail & Related papers  (2023-07-28T00:44:57Z)
• Learning to Identify Graphs from Node Trajectories in Multi-Robot Networks [15.36505600407192]
  We propose a learning-based approach that efficiently uncovers graph topologies with global convergence guarantees. We demonstrate the effectiveness of our approach in identifying graphs in multi-robot formation and flocking tasks.
  arXiv  Detail & Related papers  (2023-07-10T07:09:12Z)
• Generating Post-hoc Explanations for Skip-gram-based Node Embeddings by Identifying Important Nodes with Bridgeness [19.448849238643582]
  unsupervised node embedding methods such as DeepWalk, LINE, struc2vec, PTE, UserItem2vec, and RWJBG have emerged from the Skip-gram model. In this paper, we show that global explanations to the Skip-gram-based embeddings can be found by computing bridgeness under a spectral cluster-aware local perturbation. A novel gradient-based explanation method, which we call GRAPH-wGD, is proposed that allows the top-q global explanations about learned graph embedding vectors more efficiently.
  arXiv  Detail & Related papers  (2023-04-24T12:25:35Z)
• Graph Transformer GANs for Graph-Constrained House Generation [223.739067413952]
  We present a novel graph Transformer generative adversarial network (GTGAN) to learn effective graph node relations. The GTGAN learns effective graph node relations in an end-to-end fashion for the challenging graph-constrained house generation task.
  arXiv  Detail & Related papers  (2023-03-14T20:35:45Z)
• You Only Transfer What You Share: Intersection-Induced Graph Transfer Learning for Link Prediction [79.15394378571132]
  We investigate a previously overlooked phenomenon: in many cases, a densely connected, complementary graph can be found for the original graph. The denser graph may share nodes with the original graph, which offers a natural bridge for transferring selective, meaningful knowledge. We identify this setting as Graph Intersection-induced Transfer Learning (GITL), which is motivated by practical applications in e-commerce or academic co-authorship predictions.
  arXiv  Detail & Related papers  (2023-02-27T22:56:06Z)
• Optimal Propagation for Graph Neural Networks [51.08426265813481]
  We propose a bi-level optimization approach for learning the optimal graph structure. We also explore a low-rank approximation model for further reducing the time complexity.
  arXiv  Detail & Related papers  (2022-05-06T03:37:00Z)
• Graph Neural Networks with Feature and Structure Aware Random Walk [7.143879014059894]
  We show that in typical heterphilous graphs, the edges may be directed, and whether to treat the edges as is or simply make them undirected greatly affects the performance of the GNN models. We develop a model that adaptively learns the directionality of the graph, and exploits the underlying long-distance correlations between nodes.
  arXiv  Detail & Related papers  (2021-11-19T08:54:21Z)
• Detecting Communities from Heterogeneous Graphs: A Context Path-based Graph Neural Network Model [23.525079144108567]
  We build a Context Path-based Graph Neural Network (CP-GNN) model. It embeds the high-order relationship between nodes into the node embedding. It outperforms the state-of-the-art community detection methods.
  arXiv  Detail & Related papers  (2021-09-05T12:28:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.