MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approach

• URL: http://arxiv.org/abs/2008.10085v2
• Date: Tue, 5 Jan 2021 10:20:34 GMT
• Title: MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approach
• Authors: L\'eo Pio-Lopez, Alberto Valdeolivas, Laurent Tichit, \'Elisabeth Remy, Ana\"is Baudot
• Abstract summary: MultiVERSE is a fast and scalable method to learn node embeddings from multiplex and multiplex-heterogeneous networks. We evaluate MultiVERSE on several biological and social networks and demonstrate its efficiency.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Network embedding approaches are gaining momentum to analyse a large variety of networks. Indeed, these approaches have demonstrated their efficiency for tasks such as community detection, node classification, and link prediction. However, very few network embedding methods have been specifically designed to handle multiplex networks, i.e. networks composed of different layers sharing the same set of nodes but having different types of edges. Moreover, to our knowledge, existing approaches cannot embed multiple nodes from multiplex-heterogeneous networks, i.e. networks composed of several layers containing both different types of nodes and edges. In this study, we propose MultiVERSE, an extension of the VERSE method with Random Walks with Restart on Multiplex (RWR-M) and Multiplex-Heterogeneous (RWR-MH) networks. MultiVERSE is a fast and scalable method to learn node embeddings from multiplex and multiplex-heterogeneous networks. We evaluate MultiVERSE on several biological and social networks and demonstrate its efficiency. MultiVERSE indeed outperforms most of the other methods in the tasks of link prediction and network reconstruction for multiplex network embedding, and is also efficient in the task of link prediction for multiplex-heterogeneous network embedding. Finally, we apply MultiVERSE to study rare disease-gene associations using link prediction and clustering. MultiVERSE is freely available on github at https://github.com/Lpiol/MultiVERSE.

Related papers

• Multi-view Multi-label Anomaly Network Traffic Classification based on MLP-Mixer Neural Network [55.21501819988941]
  Existing network traffic classification based on convolutional neural networks (CNNs) often emphasizes local patterns of traffic data while ignoring global information associations. We propose an end-to-end network traffic classification method.
  arXiv  Detail & Related papers  (2022-10-30T01:52:05Z)
• Multiplex Heterogeneous Graph Convolutional Network [25.494590588212542]
  This work proposes a Multiplex Heterogeneous Graph Convolutional Network (MHGCN) for heterogeneous network embedding. Our MHGCN can automatically learn the useful heterogeneous meta-path interactions of different lengths in multiplex heterogeneous networks.
  arXiv  Detail & Related papers  (2022-08-12T06:17:54Z)
• Community detection in multiplex networks based on orthogonal nonnegative matrix tri-factorization [26.53951886710295]
  We introduce a new multiplex community detection approach that can identify communities that are common across layers as well as those that are unique to each layer. The proposed algorithm is evaluated on both synthetic and real multiplex networks and compared to state-of-the-art techniques.
  arXiv  Detail & Related papers  (2022-05-02T02:33:15Z)
• Deep Partial Multiplex Network Embedding [31.38701393977151]
  We present a novel Deep Partial Multiplex Network Embedding approach to deal with incomplete data. In particular, the network embeddings are learned by simultaneously minimizing the deep reconstruction loss with the autoencoder neural network. Experiments on four multiplex benchmarks demonstrate the superior performance of the proposed approach over several state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-03-05T04:46:44Z)
• Pay Attention to Relations: Multi-embeddings for Attributed Multiplex Networks [0.0]
  RAHMeN is a novel unified relation-aware embedding framework for attributed heterogeneous multiplex networks. Our model incorporates node attributes, motif-based features, relation-based GCN approaches, and relational self-attention to learn embeddings of nodes. We evaluate our model on four real-world datasets from Amazon, Twitter, YouTube, and Tissue PPIs in both transductive and inductive settings.
  arXiv  Detail & Related papers  (2022-03-03T18:31:29Z)
• Gated recurrent units and temporal convolutional network for multilabel classification [122.84638446560663]
  This work proposes a new ensemble method for managing multilabel classification. The core of the proposed approach combines a set of gated recurrent units and temporal convolutional neural networks trained with variants of the Adam gradients optimization approach.
  arXiv  Detail & Related papers  (2021-10-09T00:00:16Z)
• Universal Multilayer Network Exploration by Random Walk with Restart [0.0]
  MultiXrank is a Python package that enables Random Walk with Restart on any kind of multilayer network. We show how MultiXrank can be used for unsupervised node prioritization and supervised classification in the context of human genetic diseases.
  arXiv  Detail & Related papers  (2021-07-09T17:33:45Z)
• MixMo: Mixing Multiple Inputs for Multiple Outputs via Deep Subnetworks [97.08677678499075]
  We introduce MixMo, a new framework for learning multi-input multi-output deepworks. We show that binary mixing in features - particularly with patches from CutMix - enhances results by makingworks stronger and more diverse. In addition to being easy to implement and adding no cost at inference, our models outperform much costlier data augmented deep ensembles.
  arXiv  Detail & Related papers  (2021-03-10T15:31:02Z)
• A Multi-Semantic Metapath Model for Large Scale Heterogeneous Network Representation Learning [52.83948119677194]
  We propose a multi-semantic metapath (MSM) model for large scale heterogeneous representation learning. Specifically, we generate multi-semantic metapath-based random walks to construct the heterogeneous neighborhood to handle the unbalanced distributions. We conduct systematical evaluations for the proposed framework on two challenging datasets: Amazon and Alibaba.
  arXiv  Detail & Related papers  (2020-07-19T22:50:20Z)
• Detecting Communities in Heterogeneous Multi-Relational Networks:A Message Passing based Approach [89.19237792558687]
  Community is a common characteristic of networks including social networks, biological networks, computer and information networks. We propose an efficient message passing based algorithm to simultaneously detect communities for all homogeneous networks.
  arXiv  Detail & Related papers  (2020-04-06T17:36:24Z)
• Unpaired Multi-modal Segmentation via Knowledge Distillation [77.39798870702174]
  We propose a novel learning scheme for unpaired cross-modality image segmentation. In our method, we heavily reuse network parameters, by sharing all convolutional kernels across CT and MRI. We have extensively validated our approach on two multi-class segmentation problems.
  arXiv  Detail & Related papers  (2020-01-06T20:03:17Z)

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.