scholarly journals An Efficient Recommendation Algorithm Based on Heterogeneous Information Network

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Ying Yin ◽  
Wanning Zheng
Keyword(s):  
Real Data ◽  
Structural Features ◽  
Information Networks ◽  
Information Network ◽  
Complex Objects ◽  
Recommendation Algorithm ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
Heterogeneous Information Networks ◽  
Manual Search

Heterogeneous information networks can naturally simulate complex objects, and they can enrich recommendation systems according to the connections between different types of objects. At present, a large number of recommendation algorithms based on heterogeneous information networks have been proposed. However, the existing algorithms cannot extract and combine the structural features in heterogeneous information networks. Therefore, this paper proposes an efficient recommendation algorithm based on heterogeneous information network, which uses the characteristics of graph convolution neural network to automatically learn node information to extract heterogeneous information and avoid errors caused by the manual search for metapaths. Furthermore, by fully considering the scoring relationship between nodes, a calculation strategy combining heterogeneous information and a scoring information fusion strategy is proposed to solve the scoring between nodes, which makes the prediction scoring more accurate. Finally, by updating the nodes, the training scale is reduced, and the calculation efficiency is improved. The study conducted a large number of experiments on three real data sets with millions of edges. The results of the experiments show that compared with PMF, SemRec, and other algorithms, the proposed algorithm improves the recommendation accuracy MAE by approximately 3% and the RMSE by approximately 8% and reduces the time consumption significantly.

A comparative study on heterogeneous information network embeddings

2020 ◽  
Vol 39 (3) ◽  
pp. 3463-3473
Author(s):  
Fujiao Ji ◽  
Zhongying Zhao ◽  
Hui Zhou ◽  
Heng Chi ◽  
Chao Li
Keyword(s):  
Comparative Study ◽  
Communication Networks ◽  
Problem Definition ◽  
Information Networks ◽  
Future Research ◽  
Information Network ◽  
Network Embedding ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
Heterogeneous Information Networks

Heterogeneous information networks are widely used to represent real world applications in forms of social networks, word co-occurrence networks, and communication networks, etc. However, It is difficult for traditional machine learning methods to analyze these networks effectively. Heterogeneous information network embedding aims to convert the network into low dimensional vectors, which facilitates the following tasks. Thus it is receiving tremendous attention from the research community due to its effectiveness and efficiency. Although numerous methods have been present and applied successfully, there are few works to make a comparative study on heterogeneous information network embedding, which is very important for developers and researchers to select an appropriate method. To address the above problem, we make a comparative study on the heterogeneous information network embeddings. Specifically, we first give the problem definition of heterogeneous information network embedding. Then the heterogeneous information networks are classified into four categories from the perspective of network type. The state-of-the-art methods for each category are also compared and reviewed. Finally, we make a conclusion and suggest some potential future research directions.

scholarly journals Heterogeneous information network based clustering for precision traditional Chinese medicine

2019 ◽  
Vol 19 (S6) ◽  
Author(s):  
Xintian Chen ◽  
Chunyang Ruan ◽  
Yanchun Zhang ◽  
Huijuan Chen
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Large Scale ◽  
Graphical Model ◽  
Information Networks ◽  
Structured Learning ◽  
Information Network ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
Heterogeneous Information Networks

Abstract Background Traditional Chinese medicine (TCM) is a highly important complement to modern medicine and is widely practiced in China and in many other countries. The work of Chinese medicine is subject to the two factors of the inheritance and development of clinical experience of famous Chinese medicine practitioners and the difficulty in improving the service capacity of basic Chinese medicine practitioners. Heterogeneous information networks (HINs) are a kind of graphical model for integrating and modeling real-world information. Through HINs, we can integrate and model the large-scale heterogeneous TCM data into structured graph data and use this as a basis for analysis. Methods Mining categorizations from TCM data is an important task for precision medicine. In this paper, we propose a novel structured learning model to solve the problem of formula regularity, a pivotal task in prescription optimization. We integrate clustering with ranking in a heterogeneous information network. Results The results from experiments on the Pharmacopoeia of the People’s Republic of China (ChP) demonstrate the effectiveness and accuracy of the proposed model for discovering useful categorizations of formulas. Conclusions We use heterogeneous information networks to model TCM data and propose a TCM-HIN. Combining the heterogeneous graph with the probability graph, we proposed the TCM-Clus algorithm, which combines clustering with ranking and classifies traditional Chinese medicine prescriptions. The results of the categorizations can help Chinese medicine practitioners to make clinical decision.

scholarly journals Link Trustworthiness Evaluation over Multiple Heterogeneous Information Networks

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Meng Wang ◽  
Xu Qin ◽  
Wei Jiang ◽  
Chunshu Li ◽  
Guilin Qi
Keyword(s):  
Real World ◽  
Evaluation Model ◽  
Information Networks ◽  
Information Network ◽  
Training Procedure ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
Heterogeneous Information Networks ◽  
Proposed Model ◽  
Downstream Analysis

Link trustworthiness evaluation is a crucial task for information networks to evaluate the probability of a link being true in a heterogeneous information network (HIN). This task can significantly influence the effectiveness of downstream analysis. However, the performance of existing evaluation methods is limited, as they can only utilize incomplete or one-sided information from a single HIN. To address this problem, we propose a novel multi-HIN link trustworthiness evaluation model that leverages information across multiple related HINs to accomplish link trustworthiness evaluation tasks inherently and efficiently. We present an effective method to evaluate and select informative pairs across HINs and an integrated training procedure to balance inner-HIN and inter-HIN trustworthiness. Experiments on a real-world dataset demonstrate that our proposed model outperforms baseline methods and achieves the best accuracy and F1-score in downstream tasks of HINs.

scholarly journals A Survey of Data Mining Techniques on Information Networks

2018 ◽  
Vol 7 (2.6) ◽  
pp. 293
Author(s):  
Sadhana Kodali ◽  
Madhavi Dabbiru ◽  
B Thirumala Rao
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Information Networks ◽  
Information Network ◽  
Heterogeneous Information ◽  
Data Mining Techniques ◽  
Heterogeneous Information Networks ◽  
Web Objects ◽  
The Social ◽  
Social Media Network

An Information Network is the network formed by the interconnectivity of the objects formed due to the interaction between them. In our day-to-day life we can find these information networks like the social media network, the network formed by the interaction of web objects etc. This paper presents a survey of various Data Mining techniques that can be applicable to information networks. The Data Mining techniques of both homogeneous and heterogeneous information networks are discussed in detail and a comparative study on each problem category is showcased.

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 Venture Capital Recommendation Algorithm based on Heterogeneous Information Network

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Sen Wu ◽  
Huifei Li ◽  
Lu Liu
Keyword(s):  
Venture Capital ◽  
Experimental Results ◽  
Information Network ◽  
Recommendation Algorithm ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
Investment Companies

According to its characteristics, venture capital can be described as a typical heterogeneous information network, which includes multiple kinds of nodes and various relations. Getting hints from PathRank algorithm, this paper proposes VC-Recom, a recommendation algorithm based on heterogeneous information network, which helps investment companies find suitable startup projects. Besides, the experimental results show that the proposed algorithm can produce more effective recommendation results for investment firms compared with other methods.

ABLE: Meta-Path Prediction in Heterogeneous Information Networks

2022 ◽  
Vol 16 (4) ◽  
pp. 1-21
Author(s):  
Chenji Huang ◽  
Yixiang Fang ◽  
Xuemin Lin ◽  
Xin Cao ◽  
Wenjie Zhang
Keyword(s):  
State Of The Art ◽  
Information Networks ◽  
Information Network ◽  
Embedding Method ◽  
Heterogeneous Information ◽  
Heterogeneous Information Networks ◽  
Head Node ◽  
Meta Path ◽  
Path Prediction ◽  
Ap Scores

Given a heterogeneous information network (HIN) H, a head node h , a meta-path P, and a tail node t , the meta-path prediction aims at predicting whether h can be linked to t by an instance of P. Most existing solutions either require predefined meta-paths, which limits their scalability to schema-rich HINs and long meta-paths, or do not aim at predicting the existence of an instance of P. To address these issues, in this article, we propose a novel prediction model, called ABLE, by exploiting the A ttention mechanism and B i L STM for E mbedding. Particularly, we present a concatenation node embedding method by considering the node types and a dynamic meta-path embedding method that carefully considers the importance and positions of edge types in the meta-paths by the Attention mechanism and BiLSTM model, respectively. A triplet embedding is then derived to complete the prediction. We conduct extensive experiments on four real datasets. The empirical results show that ABLE outperforms the state-of-the-art methods by up to 20% and 22% of improvement of AUC and AP scores, respectively.

scholarly journals A Noval Weighted Meta Graph Method for Classification in Heterogeneous Information Networks

2020 ◽  
Vol 10 (5) ◽  
pp. 1603
Author(s):  
Jinli Zhang ◽  
Tong Li ◽  
Zongli Jiang ◽  
Xiaohua Hu ◽  
Ali Jazayeri
Keyword(s):  
Real World ◽  
Structural Features ◽  
Information Networks ◽  
Heterogeneous Information ◽  
Heterogeneous Information Networks ◽  
Real World Applications ◽  
Different Types ◽  
Multiple Challenges ◽  
Real World Datasets

There has been increasing interest in the analysis and mining of Heterogeneous Information Networks (HINs) and the classification of their components in recent years. However, there are multiple challenges associated with distinguishing different types of objects in HINs in real-world applications. In this paper, a novel framework is proposed for the weighted Meta graph-based Classification of Heterogeneous Information Networks (MCHIN) to address these challenges. The proposed framework has several appealing properties. In contrast to other proposed approaches, MCHIN can fully compute the weights of different meta graphs and mine the latent structural features of different nodes by using these weighted meta graphs. Moreover, MCHIN significantly enlarges the training sets by introducing the concept of Extension Meta Graphs in HINs. The extension meta graphs are used to augment the semantic relationship among the source objects. Finally, based on the ranking distribution of objects, MCHIN groups the objects into pre-specified classes. We verify the performance of MCHIN on three real-world datasets. As is shown and discussed in the results section, the proposed framework can effectively outperform the baselines algorithms.

Unified Embedding Model over Heterogeneous Information Network for Personalized Recommendation

2019 ◽  
Author(s):  
Zekai Wang ◽  
Hongzhi Liu ◽  
Yingpeng Du ◽  
Zhonghai Wu ◽  
Xing Zhang
Keyword(s):  
Information Extraction ◽  
State Of The Art ◽  
Structural Features ◽  
Personalized Recommendation ◽  
Information Network ◽  
Data Sparsity ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
Meta Path ◽  
End To End

Most of heterogeneous information network (HIN) based recommendation models are based on the user and item modeling with meta-paths. However, they always model users and items in isolation under each meta-path, which may lead to information extraction misled. In addition, they only consider structural features of HINs when modeling users and items during exploring HINs, which may lead to useful information for recommendation lost irreversibly. To address these problems, we propose a HIN based unified embedding model for recommendation, called HueRec. We assume there exist some common characteristics under different meta-paths for each user or item, and use data from all meta-paths to learn unified users’ and items’ representations. So the interrelation between meta-paths are utilized to alleviate the problems of data sparsity and noises on one meta-path. Different from existing models which first explore HINs then make recommendations, we combine these two parts into an end-to-end model to avoid useful information lost in initial phases. In addition, we embed all users, items and meta-paths into related latent spaces. Therefore, we can measure users’ preferences on meta-paths to improve the performances of personalized recommendation. Extensive experiments show HueRec consistently outperforms state-of-the-art methods.

Content-expressive behavior and ideological extremity: An examination of the roles of emotional intelligence and information network heterogeneity

2016 ◽  
Vol 20 (2) ◽  
pp. 815-834 ◽  
Author(s):  
Matthew Barnidge ◽  
Alberto Ardèvol-Abreu ◽  
Homero Gil de Zúñiga
Keyword(s):  
Emotional Intelligence ◽  
Information Networks ◽  
Information Network ◽  
Panel Survey ◽  
Expressive Behavior ◽  
Heterogeneous Information ◽  
Network Heterogeneity ◽  
Heterogeneous Information Networks ◽  
The Relationship

One thriving area of research on participatory media revolves around political expression and the creation of political content. This study analyzes the connections between these behaviors, heterogeneous information networks, and ideological extremity while accounting for the role of emotional intelligence. Results from a two-wave-panel survey of US adults show that people who engage in content-expressive behavior are embedded in heterogeneous information networks and that emotional intelligence moderates the relationship between content-expressive behavior and ideological extremity.

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