异构信息网络的少量学习

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Yang Fang, Xiang Zhao, Weidong Xiao, Maarten de Rijke
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引用次数: 0

摘要

异构信息网络(HIN)是许多特定领域检索和推荐场景以及对话环境中的关键资源。目前挖掘图数据的方法通常依赖于丰富的监督信息。然而,对于一项新任务来说,图学习的监督信号往往很稀缺,而且可能只有少数标注节点可用。元学习机制能够利用可适应新任务的先验知识。在本文中,我们设计了一个元学习框架,称为 META-HIN,用于解决 HIN 上的少量学习问题。据我们所知,我们是第一批设计出统一框架来实现 HINs 少量学习并促进不同图领域下游任务的人。以往的大多数模型只关注单个图上的单一任务,而 META-HIN 则不同,它能处理多个图上的不同任务(以节点分类、链接预测和异常检测为例)。对子图进行采样,以建立支持和查询集。在由元学习模块处理之前,先通过结构模块对子图进行建模,以捕捉结构特征。然后,使用异构 GNN 模块作为基础模型来表达子图的特征。我们还设计了一个基于 GAN 的对比学习模块,该模块能够利用子图的无监督信息。在实验中,我们融合了多个领域的数据集,以验证 META-HIN 在多图场景中的广泛适用性。在我们考虑的所有任务和数据集上,META-HIN 的性能始终显著优于最先进的替代方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Few-shot Learning for Heterogeneous Information Networks

Heterogeneous information networks (HINs) are a key resource in many domain-specific retrieval and recommendation scenarios, and in conversational environments. Current approaches to mining graph data often rely on abundant supervised information. However, supervised signals for graph learning tend to be scarce for a new task and only a handful of labeled nodes may be available. Meta-learning mechanisms are able to harness prior knowledge that can be adapted to new tasks.

In this paper, we design a meta-learning framework, called META-HIN, for few-shot learning problems on HINs. To the best of our knowledge, we are among the first to design a unified framework to realize the few-shot learning of HINs and facilitate different downstream tasks across different domains of graphs. Unlike most previous models, which focus on a single task on a single graph, META-HIN is able to deal with different tasks (node classification, link prediction, and anomaly detection are used as examples) across multiple graphs. Subgraphs are sampled to build the support and query set. Before being processed by the meta-learning module, subgraphs are modeled via a structure module to capture structural features. Then, a heterogeneous GNN module is used as the base model to express the features of subgraphs. We also design a GAN-based contrastive learning module that is able to exploit unsupervised information of the subgraphs.

In our experiments, we fuse several datasets from multiple domains to verify META-HIN’s broad applicability in a multiple-graph scenario. META-HIN consistently and significantly outperforms state-of-the-art alternatives on every task and across all datasets that we consider.

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来源期刊
ACM Transactions on Information Systems
ACM Transactions on Information Systems 工程技术-计算机:信息系统
CiteScore
9.40
自引率
14.30%
发文量
165
审稿时长
>12 weeks
期刊介绍: The ACM Transactions on Information Systems (TOIS) publishes papers on information retrieval (such as search engines, recommender systems) that contain: new principled information retrieval models or algorithms with sound empirical validation; observational, experimental and/or theoretical studies yielding new insights into information retrieval or information seeking; accounts of applications of existing information retrieval techniques that shed light on the strengths and weaknesses of the techniques; formalization of new information retrieval or information seeking tasks and of methods for evaluating the performance on those tasks; development of content (text, image, speech, video, etc) analysis methods to support information retrieval and information seeking; development of computational models of user information preferences and interaction behaviors; creation and analysis of evaluation methodologies for information retrieval and information seeking; or surveys of existing work that propose a significant synthesis. The information retrieval scope of ACM Transactions on Information Systems (TOIS) appeals to industry practitioners for its wealth of creative ideas, and to academic researchers for its descriptions of their colleagues'' work.
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