scholarly journals TransPath: Representation Learning for Heterogeneous Information Networks via Translation Mechanism

2018 ◽  
Author(s):  
Yang Fang ◽  
Xiang Zhao ◽  
Zhen Tan
Keyword(s):  
Large Scale ◽  
Representation Learning ◽  
Information Networks ◽  
Heterogeneous Information ◽  
Structure Information ◽  
Heterogeneous Information Networks ◽  
Network Representation ◽  
Meta Path ◽  
Translation Mechanism ◽  
Real World Datasets

In this paper, we propose a novel network representation learning model TransPath to encode heterogeneous information networks (HINs). Traditional network representation learning models aim to learn the embeddings of a homogeneous network. TransPath is able to capture the rich semantic and structure information of a HIN via meta-paths. We take advantage of the concept of translation mechanism in knowledge graph which regards a meta-path, instead of an edge, as a translating operation from the first node to the last node. Moreover, we propose a user-guided meta-path sampling strategy which takes users' preference as a guidance, which could explore the semantics of a path more precisely, and meanwhile improve model efficiency via the avoidance of other noisy and meaningless meta-paths. We evaluate our model on two large-scale real-world datasets DBLP and YELP, and two benchmark tasks similarity search and node classification. We observe that TransPath outperforms other state-of-the-art baselines consistently and significantly.

A flexible aggregation framework on large-scale heterogeneous information networks

2016 ◽  
Vol 43 (2) ◽  
pp. 186-203 ◽  
Author(s):  
Dan Yin ◽  
Hong Gao
Keyword(s):  
Large Scale ◽  
Linear Time ◽  
Information Networks ◽  
Heterogeneous Information ◽  
Heterogeneous Information Networks ◽  
Aggregation Problem ◽  
On Line ◽  
Real World Datasets ◽  
Two Phases ◽  
Time And Space Complexity

OLAP (On-line Analytical Processing) can provide users with aggregate results from different perspectives and granularities. With the advent of heterogeneous information networks that consist of multi-type, interconnected nodes, such as bibliographic networks and knowledge graphs, it is important to study flexible aggregation in such networks. The aggregation results by existing work are limited to one type of node, which cannot be applied to aggregation on multi-type nodes, and relations in large-scale heterogeneous information networks. In this paper, we investigate the flexible aggregation problem on large-scale heterogeneous information networks, which is defined on multi-type nodes and relations. Moreover, by considering both attributes and structures, we propose a novel function based on graph entropy to measure the similarities of nodes. Further, we prove that the aggregation problem based on the function is NP-hard. Therefore, we develop an efficient heuristic algorithm for aggregation in two phases: informational aggregation and structural aggregation. The algorithm has linear time and space complexity. Extensive experiments on real-world datasets demonstrate the effectiveness and efficiency of the proposed algorithm.

scholarly journals Reinforcement Learning Based Meta-Path Discovery in Large-Scale Heterogeneous Information Networks

2020 ◽  
Vol 34 (04) ◽  
pp. 6094-6101
Author(s):  
Guojia Wan ◽  
Bo Du ◽  
Shirui Pan ◽  
Gholameza Haffari
Keyword(s):  
Reinforcement Learning ◽  
Domain Knowledge ◽  
Large Scale ◽  
Information Networks ◽  
Superior Performance ◽  
Heterogeneous Information ◽  
Heterogeneous Information Networks ◽  
Lowest Common Ancestor ◽  
Path Discovery ◽  
Meta Path

Meta-paths are important tools for a wide variety of data mining and network analysis tasks in Heterogeneous Information Networks (HINs), due to their flexibility and interpretability to capture the complex semantic relation among objects. To date, most HIN analysis still relies on hand-crafting meta-paths, which requires rich domain knowledge that is extremely difficult to obtain in complex, large-scale, and schema-rich HINs. In this work, we present a novel framework, Meta-path Discovery with Reinforcement Learning (MPDRL), to identify informative meta-paths from complex and large-scale HINs. To capture different semantic information between objects, we propose a novel multi-hop reasoning strategy in a reinforcement learning framework which aims to infer the next promising relation that links a source entity to a target entity. To improve the efficiency, moreover, we develop a type context representation embedded approach to scale the RL framework to handle million-scale HINs. As multi-hop reasoning generates rich meta-paths with various length, we further perform a meta-path induction step to summarize the important meta-paths using Lowest Common Ancestor principle. Experimental results on two large-scale HINs, Yago and NELL, validate our approach and demonstrate that our algorithm not only achieves superior performance in the link prediction task, but also identifies useful meta-paths that would have been ignored by human experts.

scholarly journals Multityped Community Discovery in Time-Evolving Heterogeneous Information Networks Based on Tensor Decomposition

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Jibing Wu ◽  
Lianfei Yu ◽  
Qun Zhang ◽  
Peiteng Shi ◽  
Lihua Liu ◽  
...  
Keyword(s):  
Real World ◽  
Tensor Decomposition ◽  
Information Networks ◽  
Community Discovery ◽  
Star Network ◽  
Heterogeneous Information ◽  
Heterogeneous Information Networks ◽  
General Network ◽  
Real World Datasets ◽  
Discovery Method

The heterogeneous information networks are omnipresent in real-world applications, which consist of multiple types of objects with various rich semantic meaningful links among them. Community discovery is an effective method to extract the hidden structures in networks. Usually, heterogeneous information networks are time-evolving, whose objects and links are dynamic and varying gradually. In such time-evolving heterogeneous information networks, community discovery is a challenging topic and quite more difficult than that in traditional static homogeneous information networks. In contrast to communities in traditional approaches, which only contain one type of objects and links, communities in heterogeneous information networks contain multiple types of dynamic objects and links. Recently, some studies focus on dynamic heterogeneous information networks and achieve some satisfactory results. However, they assume that heterogeneous information networks usually follow some simple schemas, such as bityped network and star network schema. In this paper, we propose a multityped community discovery method for time-evolving heterogeneous information networks with general network schemas. A tensor decomposition framework, which integrates tensor CP factorization with a temporal evolution regularization term, is designed to model the multityped communities and address their evolution. Experimental results on both synthetic and real-world datasets demonstrate the efficiency of our framework.

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