Deep Collective Classification in Heterogeneous Information Networks

Zhang, Yizhou; Xiong, Yun; Kong, Xiangnan; Li, Shanshan; Mi, Jinhong; Zhu, Yangyong

doi:10.1145/3178876.3186106

Cited by 130 publications

(51 citation statements)

References 24 publications

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“…metapath2vec [2], HetGNN [35], HIN2vec [4], eoe [32]; and their applications, e.g. relation inference [26], classification [36], clustering [20], author identification [1]. Among them, HIN based recommendation has been increasingly attracting researchers' attention in both academic and industry fields.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph

Jin

Qin

et al. 2020

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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There is an influx of heterogeneous information network (HIN) based recommender systems in recent years since HIN is capable of characterizing complex graphs and contains rich semantics. Although the existing approaches have achieved performance improvement, while practical, they still face the following problems. On one hand, most existing HIN-based methods rely on explicit path reachability to leverage path-based semantic relatedness between users and items, e.g., metapath-based similarities. These methods are hard to use and integrate since path connections are sparse or noisy, and are often of different lengths. On the other hand, other graph-based methods aim to learn effective heterogeneous network representations by compressing node together with its neighborhood information into single embedding before prediction. This weakly coupled manner in modeling overlooks the rich interactions among nodes, which introduces an early summarization issue. In this paper, we propose an end-to-end Neighborhood-based Interaction Model for Recommendation (NIRec) to address above problems. Specifically, we first analyze the significance of learning interactions in HINs and then propose a novel formulation to capture the interactive patterns between each pair of nodes through their metapath-guided neighborhoods. Then, to explore complex interactions between metapaths and deal with the learning complexity on large-scale networks, we formulate interaction in a convolutional way and learn efficiently with fast Fourier transform. The extensive experiments on four different types of heterogeneous graphs demonstrate the performance gains of NIRec comparing with state-of-the-arts. To the best of our knowledge, this is the first work providing an efficient neighborhood-based interaction model in the HIN-based recommendations. CCS CONCEPTS • Information systems → Data mining; • Computer systems organization → Heterogeneous (hybrid) systems.

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Section: Related Workmentioning

confidence: 99%

An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph

Jin

Qin

et al. 2020

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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“…The combination of structural features and content of the labeled nodes to classify the unlabeled node 31,32 . The collective classification achieves higher classification accuracy compared with the individual classification methods shown in the previous techniques 33,34 . A collective classification method to decrease the learning and inference changes within the domains whereas the same set of nodes are connected by multiple networks 35 .…”

Section: Related Workmentioning

confidence: 99%

“…A collective classification method to decrease the learning and inference changes within the domains whereas the same set of nodes are connected by multiple networks 35 . Transfer learning is efficaciously useful in many application area of machine learning like, image classification, 36 text classification, 32 and human activity classification 33–35,37–39 …”

Section: Related Workmentioning

confidence: 99%

Author classification using transfer learning and predicting stars in co‐author networks

et al. 2020

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Summary The vast amount of data is key challenge to mine a new scholar that is plausible to be star in the upcoming period. The enormous amount of unstructured data raise every year is infeasible for traditional learning; consequently, we need a high quality of preprocessing technique to expand the performance of traditional learning. We have persuaded a novel approach, Authors classification algorithm using Transfer Learning (ACTL) to learn new task on target area to mine the external knowledge from the source domain. Comprehensive experimental outcomes on real‐world networks showed that ACTL, Node‐based Influence Predicting Stars, Corresponding Authors Mutual Influence based on Predicting Stars, and Specific Topic Domain‐based Predicting Stars enhanced the node classification accuracy as well as predicting rising stars to compared with contemporary baseline methods.

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“…There have been many approaches proposed in varied applications to perform collective classification [1,20,28,31,36]. The primary issue with such methods is that the amount of time required to perform inference grows rapidly with increases in number of nodes and relations.…”

Section: Relational Structure and Micrographsmentioning

confidence: 99%

“…Incorporating such structural side information typically yields a boost in model performance. Indeed, learning with side information has shown to be successful in several applications such as recommender systems [22,30], knowledge graphs [25], entity resolution [29], computer vision [15], and has recently been applied even in deep learning tasks [34,36].…”

Section: Introductionmentioning

confidence: 99%

Identifying Facet Mismatches In Search Via Micrographs

Srinivasan

Rao

Subbian

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

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E-commerce search engines are the primary means by which customers shop for products online. Each customer query contains multiple facets such as product type, color, brand, etc. A successful search engine retrieves products that are relevant to the query along each of these attributes. However, due to lexical (erroneous title, description, etc.) and behavioral irregularities (clicks or purchases of products that do not belong to the same facet as the query), some mismatched products are often included in search results. These irregularities can be detected using simple binary classifiers like gradient boosted decision trees or logistic regression. Typically, these binary classifiers use strong independence assumptions between the results and ignore structural relationships available in the data, such as the connections between products and queries. In this paper, we use the connections that exist between products and query to identify a special kind of structure we refer to as a micrograph. Further, we make use of Statistical Relational Learning (SRL) to incorporate these micrographs in the data and pose the problem as a structured prediction problem. We refer to this approach as structured mismatch classification (smc). In addition, we show that naive addition of structure does not improve the performance of the model and hence introduce a variation of smc, strong smc (s 2 mc), which improves over the baseline by passing information from high-confidence predictions to lower confidence predictions. In our empirical evaluation we show that our proposed approach outperforms the baseline classification methods by up to 12% in precision. Furthermore, we use quasi-Newton methods to make our method viable for real-time inference in a search engine and show that our approach is up to 150 times faster than existing ADMM-based solvers.

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Deep Collective Classification in Heterogeneous Information Networks

Cited by 130 publications

References 24 publications

An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph

An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph

Author classification using transfer learning and predicting stars in co‐author networks

Identifying Facet Mismatches In Search Via Micrographs

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