A Multi-Semantic Metapath Model for Large Scale Heterogeneous Network Representation Learning

• URL: http://arxiv.org/abs/2007.11380v1
• Date: Sun, 19 Jul 2020 22:50:20 GMT
• Title: A Multi-Semantic Metapath Model for Large Scale Heterogeneous Network Representation Learning
• Authors: Xuandong Zhao, Jinbao Xue, Jin Yu, Xi Li, Hongxia Yang
• Abstract summary: 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.
• Score: 52.83948119677194
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Network Embedding has been widely studied to model and manage data in a variety of real-world applications. However, most existing works focus on networks with single-typed nodes or edges, with limited consideration of unbalanced distributions of nodes and edges. In real-world applications, networks usually consist of billions of various types of nodes and edges with abundant attributes. To tackle these challenges, in this paper 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 and propose a unified framework for the embedding learning. We conduct systematical evaluations for the proposed framework on two challenging datasets: Amazon and Alibaba. The results empirically demonstrate that MSM can achieve relatively significant gains over previous state-of-arts on link prediction.

Related papers

• Unified Generative and Discriminative Training for Multi-modal Large Language Models [88.84491005030316]
  Generative training has enabled Vision-Language Models (VLMs) to tackle various complex tasks. Discriminative training, exemplified by models like CLIP, excels in zero-shot image-text classification and retrieval. This paper proposes a unified approach that integrates the strengths of both paradigms.
  arXiv  Detail & Related papers  (2024-11-01T01:51:31Z)
• On Optimizing the Communication of Model Parallelism [74.15423270435949]
  We study a novel and important communication pattern in large-scale model-parallel deep learning (DL) In cross-mesh resharding, a sharded tensor needs to be sent from a source device mesh to a destination device mesh. We propose two contributions to address cross-mesh resharding: an efficient broadcast-based communication system, and an "overlapping-friendly" pipeline schedule.
  arXiv  Detail & Related papers  (2022-11-10T03:56:48Z)
• 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)
• Bayesian community detection for networks with covariates [16.230648949593153]
  "Community detection" has arguably received the most attention in the scientific community. We propose a block model with a co-dependent random partition prior. Our model has the ability to learn the number of the communities via posterior inference without having to assume it to be known.
  arXiv  Detail & Related papers  (2022-03-04T01:58:35Z)
• Multiplex Graph Networks for Multimodal Brain Network Analysis [30.195666008281915]
  We propose MGNet, a simple and effective multiplex graph convolutional network (GCN) model for multimodal brain network analysis. We conduct classification task on two challenging real-world datasets (HIV and Bipolar disorder)
  arXiv  Detail & Related papers  (2021-07-31T06:01:29Z)
• Meta-Path-Free Representation Learning on Heterogeneous Networks [5.106061955284303]
  We propose a novel meta-path-free representation learning on heterogeneous networks, namely Heterogeneous graph Convolutional Networks (HCN) The proposed method fuses the heterogeneous and develops a $k$-strata algorithm ($k$ is an integer) to capture the $k$-hop structural and semantic information. The experimental results demonstrate that the proposed method significantly outperforms the current state-of-the-art methods in a variety of analytic tasks.
  arXiv  Detail & Related papers  (2021-02-16T12:37:38Z)
• GAHNE: Graph-Aggregated Heterogeneous Network Embedding [32.44836376873812]
  Heterogeneous network embedding aims to embed nodes into low-dimensional vectors which capture rich intrinsic information of heterogeneous networks. Existing models either depend on manually designing meta-paths, ignore mutual effects between different semantics, or omit some aspects of information from global networks. In GAHNE model, we develop several mechanisms that can aggregate semantic representations from different single-type sub-networks as well as fuse the global information into final embeddings.
  arXiv  Detail & Related papers  (2020-12-23T07:11:30Z)
• 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)
• Diversity inducing Information Bottleneck in Model Ensembles [73.80615604822435]
  In this paper, we target the problem of generating effective ensembles of neural networks by encouraging diversity in prediction. We explicitly optimize a diversity inducing adversarial loss for learning latent variables and thereby obtain diversity in the output predictions necessary for modeling multi-modal data. Compared to the most competitive baselines, we show significant improvements in classification accuracy, under a shift in the data distribution.
  arXiv  Detail & Related papers  (2020-03-10T03:10:41Z)
• MS-Net: Multi-Site Network for Improving Prostate Segmentation with Heterogeneous MRI Data [75.73881040581767]
  We propose a novel multi-site network (MS-Net) for improving prostate segmentation by learning robust representations. Our MS-Net improves the performance across all datasets consistently, and outperforms state-of-the-art methods for multi-site learning.
  arXiv  Detail & Related papers  (2020-02-09T14:11:50Z)

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.