CHIN: Classification with META-PATH in Heterogeneous Information Networks

2018 ◽  
pp. 63-74 ◽  
Author(s):  
Jinli Zhang ◽  
Zongli Jiang ◽  
Tong Li
Keyword(s):  
Information Networks ◽  
Heterogeneous Information ◽  
Heterogeneous Information Networks ◽  
Meta Path

Meta-Path Based Inductive Classification in Heterogeneous Information Networks

2016 ◽  
Vol 13 (10) ◽  
pp. 6747-6753
Author(s):  
Pingjian Ding ◽  
Xiangtao Chen ◽  
Zipin Guan
Keyword(s):  
Heterogeneous Networks ◽  
State Of The Art ◽  
Test Sample ◽  
Information Networks ◽  
Classification Problems ◽  
Heterogeneous Information ◽  
Heterogeneous Information Networks ◽  
Meta Path ◽  
Transductive Inference ◽  
The Given

The goal of inductive classification approaches is to infer the correct mapping from test set to labels, while the goal of transductive inference is to predict the correct labels for the given unlabeled data. Hence, the increased unlabeled samples can’t be classified by transductive classification. In this paper, we focus on studying the inductive classification problems in heterogeneous networks, which involve multiple types of objects interconnected by multiple types of links. Moreover, the objects and the links are gradually increasing over time. To accommodate characteristics of heterogeneous networks, a meta-path-based heterogeneous inductive classification (Hic) was proposed. First, the different sub-networks were constructed according to the selected meta-path. Second, the characteristic paths of each sub-network were extracted via the specified minimum support, and were assigned appropriate weights. Then, Hic model based on characteristic path was built. Finally, the Hic scores of each classification label for each test sample was calculated via links between test samples and sub-networks. Experiments on the DBLP showed that the proposed method significantly improves the accuracy and stability over the existing state-of-the-art methods for classification in dynamic heterogeneous network.

Distributed Algorithms for Finding Meta-Paths of a Large Heterogeneous Information Network on Cloud

2020 ◽  
pp. 223-249
Author(s):  
Phuc Do
Keyword(s):  
Decision Making ◽  
Distributed Algorithms ◽  
Information Networks ◽  
Information Network ◽  
Breadth First Search ◽  
Cloud Computing Environment ◽  
Heterogeneous Information ◽  
Heterogeneous Information Networks ◽  
Path Discovery ◽  
Meta Path

Meta-path is an important concept of heterogeneous information networks (HINs). Meta-paths were used in many tasks such as information retrieval, decision making, and product recommendation. Normally meta-paths were proposed by human experts. Recently, works on meta-path discovery have proposed in-memory solutions that fit in one computer. With large HINs, the whole HIN cannot be loaded in the memory. In this chapter, the authors proposed distributed algorithms to discover meta-paths of large HINs on cloud. They develop the distributed algorithms to discover the significant meta-path, maximal significant meta-path, and top-k meta-paths between two vertices of HIN. Calculation of the support of meta-paths or performing breadth first search can be computational costly in very large HINs. Conveniently, the distributed algorithms utilize the GraphFrames library of Apache Spark on cloud computing environment to efficiently query large HINs. The authors conduct the experiments on large DBLP dataset to prove the performance of our algorithms on cloud.

scholarly journals A Top-N Movie Recommendation Framework Based on Deep Neural Network with Heterogeneous Modeling

2021 ◽  
Vol 11 (16) ◽  
pp. 7418
Author(s):  
Jibing Gong ◽  
Xinghao Zhang ◽  
Qing Li ◽  
Cheng Wang ◽  
Yaxi Song ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Information Networks ◽  
Implicit Feedback ◽  
Feedback Information ◽  
Heterogeneous Information ◽  
Implicit Information ◽  
Heterogeneous Information Networks ◽  
Meta Path ◽  
Explicit Feedback

To provide more accurate and stable recommendations, it is necessary to combine display information with implicit information and to dig out potential information. Existing methods only consider explicit feedback information or implicit feedback information unilaterally and ignore the potential information of explicit feedback information and implicit feedback information, which is also crucial to the accuracy of the recommendation system. However, the traditional Heterogeneous Information Networks (HIN) recommendation ignores the attribute information in the meta-path and the interaction between the user and the item and, instead, only considers the linear characteristics of the user-object often ignoring its non-linear characteristics. Aiming at the potential information acquisition problem from assorted feedback, we propose a new top-N recommendation method MFDNN for Heterogeneous Information Networks (HINs). First, we consider explicit and implicit feedback information to determine the potential preferences of users and the potential features of the product. Then, matrix factorization (MF) and a deep neural network (DNN) are fused to learn independent feature embeddings through MF and DNN, and fully considering the linear and non-linear characteristics of the user-object. MFDNN was tested on several real data sets, such as Movie-Lens, and compared with benchmark experiments. MFDNN significantly improved the hit ratio (HR) and normalized discounted cumulative gain (NDCG). Further research showed that the meta-path bias had an excellent effect on the gain of potential information mining and the fusion of explicit and implicit information in the accuracy and stability of user interest classification.

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