Modeling Dynamic Heterogeneous Graph and Node Importance for Future Citation Prediction

• URL: http://arxiv.org/abs/2305.17417v1
• Date: Sat, 27 May 2023 08:53:26 GMT
• Title: Modeling Dynamic Heterogeneous Graph and Node Importance for Future Citation Prediction
• Authors: Hao Geng, Deqing Wang, Fuzhen Zhuang, Xuehua Ming, Chenguang Du, Ting Jiang, Haolong Guo, Rui Liu
• Abstract summary: We propose a Dynamic heterogeneous Graph and Node Importance network (DGNI) learning framework to predict future citation trends of newly published papers. First, a dynamic heterogeneous network embedding module is provided to capture the dynamic evolutionary trends of the whole academic network. A node importance embedding module is proposed to capture the global consistency relationship to figure out each paper's node importance.
• Score: 26.391252682418607
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate citation count prediction of newly published papers could help editors and readers rapidly figure out the influential papers in the future. Though many approaches are proposed to predict a paper's future citation, most ignore the dynamic heterogeneous graph structure or node importance in academic networks. To cope with this problem, we propose a Dynamic heterogeneous Graph and Node Importance network (DGNI) learning framework, which fully leverages the dynamic heterogeneous graph and node importance information to predict future citation trends of newly published papers. First, a dynamic heterogeneous network embedding module is provided to capture the dynamic evolutionary trends of the whole academic network. Then, a node importance embedding module is proposed to capture the global consistency relationship to figure out each paper's node importance. Finally, the dynamic evolutionary trend embeddings and node importance embeddings calculated above are combined to jointly predict the future citation counts of each paper, by a log-normal distribution model according to multi-faced paper node representations. Extensive experiments on two large-scale datasets demonstrate that our model significantly improves all indicators compared to the SOTA models.

Related papers

• Scalable Weibull Graph Attention Autoencoder for Modeling Document Networks [50.42343781348247]
  We develop a graph Poisson factor analysis (GPFA) which provides analytic conditional posteriors to improve the inference accuracy. We also extend GPFA to a multi-stochastic-layer version named graph Poisson gamma belief network (GPGBN) to capture the hierarchical document relationships at multiple semantic levels. Our models can extract high-quality hierarchical latent document representations and achieve promising performance on various graph analytic tasks.
  arXiv  Detail & Related papers  (2024-10-13T02:22:14Z)
• Temporal Graph Neural Network-Powered Paper Recommendation on Dynamic Citation Networks [4.666226480911492]
  This paper introduces a temporal dimension to paper recommendation strategies. The core idea is to continuously update a paper's embedding when new citation relationships appear. A learnable memory update module based on a Recurrent Neural Network (RNN) is utilized to study the evolution of the embedding.
  arXiv  Detail & Related papers  (2024-08-27T19:10:21Z)
• DURENDAL: Graph deep learning framework for temporal heterogeneous networks [0.5156484100374057]
  Temporal heterogeneous networks (THNs) are evolving networks that characterize many real-world applications. We propose DURENDAL, a graph deep learning framework for THNs.
  arXiv  Detail & Related papers  (2023-09-30T10:46:01Z)
• H2CGL: Modeling Dynamics of Citation Network for Impact Prediction [13.00224680454585]
  We construct hierarchical and heterogeneous graphs for target papers with an annual perspective. The graphs can record the annual dynamics of target papers' scientific context information. A novel graph neural network, Hierarchical and Heterogeneous Contrastive Graph Learning Model, is proposed.
  arXiv  Detail & Related papers  (2023-04-16T13:04:32Z)
• Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand Prediction [52.0977259978343]
  This paper proposes a Continuous-time and Multi-level dynamic graph representation learning method for Origin-Destination demand prediction (CMOD) The state vectors keep historical transaction information and are continuously updated according to the most recently happened transactions. Experiments are conducted on two real-world datasets from Beijing Subway and New York Taxi, and the results demonstrate the superiority of our model against the state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-06-30T03:37:50Z)
• EXPERT: Public Benchmarks for Dynamic Heterogeneous Academic Graphs [5.4744970832051445]
  We present a variety of large scale, dynamic heterogeneous academic graphs to test the effectiveness of models developed for graph forecasting tasks. Our novel datasets cover both context and content information extracted from scientific publications across two communities: Artificial Intelligence (AI) and Nuclear Nonproliferation (NN)
  arXiv  Detail & Related papers  (2022-04-14T19:43:34Z)
• Handling Distribution Shifts on Graphs: An Invariance Perspective [78.31180235269035]
  We formulate the OOD problem on graphs and develop a new invariant learning approach, Explore-to-Extrapolate Risk Minimization (EERM) EERM resorts to multiple context explorers that are adversarially trained to maximize the variance of risks from multiple virtual environments. We prove the validity of our method by theoretically showing its guarantee of a valid OOD solution.
  arXiv  Detail & Related papers  (2022-02-05T02:31:01Z)
• TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning [87.38675639186405]
  We propose a novel graph neural network approach, called TCL, which deals with the dynamically-evolving graph in a continuous-time fashion. To the best of our knowledge, this is the first attempt to apply contrastive learning to representation learning on dynamic graphs.
  arXiv  Detail & Related papers  (2021-05-17T15:33:25Z)
• Learning the Implicit Semantic Representation on Graph-Structured Data [57.670106959061634]
  Existing representation learning methods in graph convolutional networks are mainly designed by describing the neighborhood of each node as a perceptual whole. We propose a Semantic Graph Convolutional Networks (SGCN) that explores the implicit semantics by learning latent semantic-paths in graphs.
  arXiv  Detail & Related papers  (2021-01-16T16:18:43Z)
• A Heterogeneous Dynamical Graph Neural Networks Approach to Quantify Scientific Impact [39.9627229543809]
  We propose an approach based on Heterogeneous Dynamical Graph Neural Network (HDGNN) to explicitly model and predict the cumulative impact of papers and authors. Experiments conducted on a real citation dataset demonstrate its superior performance of predicting the impact of both papers and authors.
  arXiv  Detail & Related papers  (2020-03-26T17:15:36Z)
• Tensor Graph Convolutional Networks for Multi-relational and Robust Learning [74.05478502080658]
  This paper introduces a tensor-graph convolutional network (TGCN) for scalable semi-supervised learning (SSL) from data associated with a collection of graphs, that are represented by a tensor. The proposed architecture achieves markedly improved performance relative to standard GCNs, copes with state-of-the-art adversarial attacks, and leads to remarkable SSL performance over protein-to-protein interaction networks.
  arXiv  Detail & Related papers  (2020-03-15T02:33:21Z)

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.