Relevance Search on Signed Heterogeneous Information Network Based on Meta-path Factorization

2015 ◽  
pp. 181-192 ◽  
Author(s):  
Min Zhu ◽  
Tianchen Zhu ◽  
Zhaohui Peng ◽  
Guang Yang ◽  
Yang Xu ◽  
...  
Keyword(s):  
Information Network ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
Meta Path

W-MMP2Vec: Topic-driven network embedding model for link prediction in content-based heterogeneous information network

2021 ◽  
Vol 25 (3) ◽  
pp. 711-738
Author(s):  
Phu Pham ◽  
Phuc Do
Keyword(s):  
Link Prediction ◽  
Representation Learning ◽  
Information Network ◽  
Network Embedding ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
Learning Framework ◽  
Novel Approach ◽  
Proposed Model ◽  
Meta Path

Link prediction on heterogeneous information network (HIN) is considered as a challenge problem due to the complexity and diversity in types of nodes and links. Currently, there are remained challenges of meta-path-based link prediction in HIN. Previous works of link prediction in HIN via network embedding approach are mainly focused on exploiting features of node rather than existing relations in forms of meta-paths between nodes. In fact, predicting the existence of new links between non-linked nodes is absolutely inconvincible. Moreover, recent HIN-based embedding models also lack of thorough evaluations on the topic similarity between text-based nodes along given meta-paths. To tackle these challenges, in this paper, we proposed a novel approach of topic-driven multiple meta-path-based HIN representation learning framework, namely W-MMP2Vec. Our model leverages the quality of node representations by combining multiple meta-paths as well as calculating the topic similarity weight for each meta-path during the processes of network embedding learning in content-based HINs. To validate our approach, we apply W-TMP2Vec model in solving several link prediction tasks in both content-based and non-content-based HINs (DBLP, IMDB and BlogCatalog). The experimental outputs demonstrate the effectiveness of proposed model which outperforms recent state-of-the-art HIN representation learning models.

scholarly journals Automatic Generation of Meta-Path Graph for Concept Recommendation in MOOCs

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1671
Author(s):  
Jibing Gong ◽  
Cheng Wang ◽  
Zhiyong Zhao ◽  
Xinghao Zhang
Keyword(s):  
Auxiliary Information ◽  
Automatic Generation ◽  
Information Network ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
Online Study ◽  
Path Graph ◽  
Proposed Model ◽  
Meta Path

In MOOCs, generally speaking, curriculum designing, course selection, and knowledge concept recommendation are the three major steps that systematically instruct users to learn. This paper focuses on the knowledge concept recommendation in MOOCs, which recommends related topics to users to facilitate their online study. The existing approaches only consider the historical behaviors of users, but ignore various kinds of auxiliary information, which are also critical for user embedding. In addition, traditional recommendation models only consider the immediate user response to the recommended items, and do not explicitly consider the long-term interests of users. To deal with the above issues, this paper proposes AGMKRec, a novel reinforced concept recommendation model with a heterogeneous information network. We first clarify the concept recommendation in MOOCs as a reinforcement learning problem to offer a personalized and dynamic knowledge concept label list to users. To consider more auxiliary information of users, we construct a heterogeneous information network among users, courses, and concepts, and use a meta-path-based method which can automatically identify useful meta-paths and multi-hop connections to learn a new graph structure for learning effective node representations on a graph. Comprehensive experiments and analyses on a real-world dataset collected from XuetangX show that our proposed model outperforms some state-of-the-art methods.

W-Com2Vec: A topic-driven meta-path- based intra-community embedding for content-based heterogeneous information network

2020 ◽  
Vol 24 (5) ◽  
pp. 1207-1233
Author(s):  
Phu Pham ◽  
Phuc Do
Keyword(s):  
Network Analysis ◽  
Biological Networks ◽  
Traditional Approach ◽  
Empirical Studies ◽  
Information Network ◽  
Network Embedding ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
Structure Preserving ◽  
Meta Path

Heterogeneous information network (HIN) are becoming popular across multiple applications in forms of complex large-scaled networked data such as social networks, bibliographic networks, biological networks, etc. Recently, information network embedding (INE) has aroused tremendously interests from researchers due to its effectiveness in information network analysis and mining tasks. From recent views of INE, community is considered as the mesoscopic preserving network’s structure which can be combined with traditional approach of network’s node proximities (microscopic structure preserving) to leverage the quality of network’s representation. Most of contemporary INE models, like as: HIN2Vec, Metapath2Vec, HINE, etc. mainly concentrate on microscopic network structure preserving and ignore the mesoscopic (intra-community) structure of HIN. In this paper, we introduce a novel approach of topic-driven meta-path-based embedding, namely W-Com2Vec (Weighted intra-community to vector). Our proposed W-Com2Vec model enables to capture richer semantic of node representation by applying the meta-path-based community-aware, node proximity preserving and topic similarity evaluation at the same time during the process of network embedding. We demonstrate comprehensive empirical studies on our proposed W-Com2Vec model with several real-world HINs. Experimental results show W-Com2Vec outperforms recent state-of-the-art INE models in solving primitive network analysis and mining tasks.

scholarly journals Cash-Out User Detection Based on Attributed Heterogeneous Information Network with a Hierarchical Attention Mechanism

2019 ◽  
Vol 33 ◽  
pp. 946-953 ◽  
Author(s):  
Binbin Hu ◽  
Zhiqiang Zhang ◽  
Chuan Shi ◽  
Jun Zhou ◽  
Xiaolong Li ◽  
...  
Keyword(s):  
Financial Services ◽  
Attention Mechanism ◽  
Feature Representation ◽  
Information Network ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
Structure Information ◽  
Detection Model ◽  
Meta Path ◽  
Interaction Relations

As one of the major frauds in financial services, cash-out fraud is that users pursue cash gains with illegal or insincere means. Conventional solutions for the cash-out user detection are to perform subtle feature engineering for each user and then apply a classifier, such as GDBT and Neural Network. However, users in financial services have rich interaction relations, which are seldom fully exploited by conventional solutions. In this paper, with the real datasets in Ant Credit Pay of Ant Financial Services Group, we first study the cashout user detection problem and propose a novel hierarchical attention mechanism based cash-out user detection model, called HACUD. Specifically, we model different types of objects and their rich attributes and interaction relations in the scenario of credit payment service with an Attributed Heterogeneous Information Network (AHIN). The HACUD model enhances feature representation of objects through meta-path based neighbors exploiting different aspects of structure information in AHIN. Furthermore, a hierarchical attention mechanism is elaborately designed to model user’s preferences towards attributes and meta-paths. Experimental results on two real datasets show that the HACUD outperforms the state-of-the-art methods.

Meta-path-based link prediction in schema-rich heterogeneous information network

2017 ◽  
Vol 3 (4) ◽  
pp. 285-296 ◽  
Author(s):  
Xiaohuan Cao ◽  
Yuyan Zheng ◽  
Chuan Shi ◽  
Jingzhi Li ◽  
Bin Wu
Keyword(s):  
Link Prediction ◽  
Information Network ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
Meta Path
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