A Survey on Hyperlink Prediction

• URL: http://arxiv.org/abs/2207.02911v1
• Date: Wed, 6 Jul 2022 18:36:35 GMT
• Title: A Survey on Hyperlink Prediction
• Authors: Can Chen, Yang-Yu Liu
• Abstract summary: We propose a new taxonomy to classify existing hyperlink prediction methods into four categories: similarity-based, probability-based, matrix optimization-based, and deep learning-based methods. Notably, deep learning-based methods prevail over other methods in hyperlink prediction.
• Score: 5.040884130649049
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As a natural extension of link prediction on graphs, hyperlink prediction aims for the inference of missing hyperlinks in hypergraphs, where a hyperlink can connect more than two nodes. Hyperlink prediction has applications in a wide range of systems, from chemical reaction networks, social communication networks, to protein-protein interaction networks. In this paper, we provide a systematic and comprehensive survey on hyperlink prediction. We propose a new taxonomy to classify existing hyperlink prediction methods into four categories: similarity-based, probability-based, matrix optimization-based, and deep learning-based methods. To compare the performance of methods from different categories, we perform a benchmark study on various hypergraph applications using representative methods from each category. Notably, deep learning-based methods prevail over other methods in hyperlink prediction.

Related papers

• Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation [50.01551945190676]
  Social robot navigation can be helpful in various contexts of daily life but requires safe human-robot interactions and efficient trajectory planning. We propose a systematic relational reasoning approach with explicit inference of the underlying dynamically evolving relational structures. We demonstrate its effectiveness for multi-agent trajectory prediction and social robot navigation.
  arXiv  Detail & Related papers  (2024-01-22T18:58:22Z)
• Temporal Link Prediction Using Graph Embedding Dynamics [0.0]
  Temporal link prediction in dynamic networks is of particular interest due to its potential for solving complex scientific and real-world problems. Traditional approaches to temporal link prediction have focused on finding the aggregation of dynamics of the network as a unified output. We propose a novel perspective on temporal link prediction by defining nodes as Newtonian objects and incorporating the concept of velocity to predict network dynamics.
  arXiv  Detail & Related papers  (2024-01-15T07:35:29Z)
• Improving Link Prediction in Social Networks Using Local and Global Features: A Clustering-based Approach [0.0]
  We propose an approach based on the combination of first and second group methods to tackle the link prediction problem. Our two-phase developed method firstly determines new features related to the position and dynamic behavior of nodes. Then, a subspace clustering algorithm is applied to group social objects based on the computed similarity measures.
  arXiv  Detail & Related papers  (2023-05-17T14:45:02Z)
• Large-Scale Sequential Learning for Recommender and Engineering Systems [91.3755431537592]
  In this thesis, we focus on the design of an automatic algorithms that provide personalized ranking by adapting to the current conditions. For the former, we propose novel algorithm called SAROS that take into account both kinds of feedback for learning over the sequence of interactions. The proposed idea of taking into account the neighbour lines shows statistically significant results in comparison with the initial approach for faults detection in power grid.
  arXiv  Detail & Related papers  (2022-05-13T21:09:41Z)
• Principled inference of hyperedges and overlapping communities in hypergraphs [0.0]
  We propose a framework based on statistical inference to characterize the structural organization of hypergraphs. We show strong performance in hyperedge prediction tasks, detecting communities well aligned with the information carried by interactions, and robustness against addition of noisy hyperedges.
  arXiv  Detail & Related papers  (2022-04-12T09:13:46Z)
• Hyperlink-induced Pre-training for Passage Retrieval in Open-domain Question Answering [53.381467950545606]
  HyperLink-induced Pre-training (HLP) is a method to pre-train the dense retriever with the text relevance induced by hyperlink-based topology within Web documents. We demonstrate that the hyperlink-based structures of dual-link and co-mention can provide effective relevance signals for large-scale pre-training.
  arXiv  Detail & Related papers  (2022-03-14T09:09:49Z)
• High-order joint embedding for multi-level link prediction [2.4366811507669124]
  Link prediction infers potential links from observed networks, and is one of the essential problems in network analyses. We propose a novel tensor-based joint network embedding approach on simultaneously encoding pairwise links and hyperlinks. Numerical studies on both simulation settings and Facebook ego-networks indicate that the proposed method improves both hyperlink and pairwise link prediction accuracy.
  arXiv  Detail & Related papers  (2021-11-07T05:22:54Z)
• Online Multi-Agent Forecasting with Interpretable Collaborative Graph Neural Network [65.11999700562869]
  We propose a novel collaborative prediction unit (CoPU), which aggregates predictions from multiple collaborative predictors according to a collaborative graph. Our methods outperform state-of-the-art works on the three tasks by 28.6%, 17.4% and 21.0% on average.
  arXiv  Detail & Related papers  (2021-07-02T08:20:06Z)
• Link prediction in multiplex networks via triadic closure [0.9329978164030673]
  Link prediction algorithms can help to understand the structure and dynamics of complex systems. We show that different kind of relational data can be exploited to improve the prediction of new links. We propose a novel link prediction algorithm by generalizing the Adamic-Adar method to multiplex networks composed by an arbitrary number of layers.
  arXiv  Detail & Related papers  (2020-11-16T20:25:08Z)
• Combining Task Predictors via Enhancing Joint Predictability [53.46348489300652]
  We present a new predictor combination algorithm that improves the target by i) measuring the relevance of references based on their capabilities in predicting the target, and ii) strengthening such estimated relevance. Our algorithm jointly assesses the relevance of all references by adopting a Bayesian framework. Based on experiments on seven real-world datasets from visual attribute ranking and multi-class classification scenarios, we demonstrate that our algorithm offers a significant performance gain and broadens the application range of existing predictor combination approaches.
  arXiv  Detail & Related papers  (2020-07-15T21:58:39Z)
• Heterogeneous Graph Neural Networks for Malicious Account Detection [64.0046412312209]
  We present GEM, the first heterogeneous graph neural network approach for detecting malicious accounts. We learn discriminative embeddings from heterogeneous account-device graphs based on two fundamental weaknesses of attackers, i.e. device aggregation and activity aggregation. Experiments show that our approaches consistently perform promising results compared with competitive methods over time.
  arXiv  Detail & Related papers  (2020-02-27T18:26:44Z)

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.