Information networks – concept, classification and application

2021 ◽  
Vol 1 (2) ◽  
pp. 59-67
Author(s):  
Salihović Nazif ◽  
Adisa Hasković Džubur
Keyword(s):  
Data Transmission ◽  
Functional Organization ◽  
Basic Structure ◽  
Information Networks ◽  
Information Network ◽  
Heterogeneous Information ◽  
Scientific Disciplines ◽  
Space Information Network ◽  
Types Of Information ◽  
Almost All

An information network is a structure used for transmitting various forms and types of information. In basic structure, it consists of branches that connect certain nodes. Many scientists and researchers have dealt with the problem of defining an information network, depending on the functional organization and data transmission, and also with the classification of information networks.  Information networks have a very wide application in almost all scientific disciplines. A large number of researches are carried out on the application of information networks (e.g. bisociative, deep information network, heterogeneous information network, and space information network) in the field of medicine for easier detection of diseases, drug development, etc, and other needs to support real-time communication, massive data transmission, and data processing. In accordance with the above mentioned, the aim of this paper is to offer different approaches in defining and classifying general forms of information networks and to notice their wide application in different research disciplines.

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.

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.

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.

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 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 The quality of experience–oriented personal message acquisition optimization for intelligent apparatus in space information networks

2018 ◽  
Vol 14 (5) ◽  
pp. 155014771877253
Author(s):  
Changhua Yao ◽  
Lei Zhu
Keyword(s):  
Statistical Analysis ◽  
Mathematical Analysis ◽  
Quality Of Experience ◽  
Statistical Data ◽  
Information Networks ◽  
Information Network ◽  
Optimization Approach ◽  
Space Information Network ◽  
Space Information Networks

In this article, we investigate the problem of personal message acquisition optimization for intelligent apparatus in the space information network. The quality of experience for persons during achieving the synthetical message rapidly and exactly through the apparatus is significant to the apparatus layout. In this article, we presented a quality of experience–oriented synthetical statistical analysis optimization approach for the personal message acquisition for intelligent apparatus. The main contribution is that the mathematical analysis is combined with the statistical data for the assessment of the personal message acquisition to overcome some drawbacks in the existing works. Experiments verified that the presented approach could make the personal message acquisition with much higher quality of experience.

A Global Seamless Hybrid Constellation Design Approach with Restricted Ground Supporting for Space Information Network

2019 ◽  
Vol 7 (3) ◽  
pp. 257-269
Author(s):  
Wei Zhang ◽  
Tao Wu ◽  
Hong Ma ◽  
Guixin Li
Keyword(s):  
High Speed ◽  
Information Networks ◽  
Design Approach ◽  
Information Network ◽  
Backbone Network ◽  
Constellation Design ◽  
Space Information Network ◽  
Space Information Networks ◽  
Temporal Coverage ◽  
Hybrid Constellation

Abstract In space information networks, satellites are generally in high speed orbit motion. In order to obtain better spacial and temporal coverage performance, satellites should cooperate with each other as a constellation. Previous works on constellations mainly focus on global seamless coverage using fewer satellites. However, like most countries, it is hard for China to build ground stations in overseas, and the geostationary Earth orbit position resource is scarce. In this paper, we investigate the constellation design problem with restricted ground supporting. We first proposes a “backbone network + enhanced network” hybrid constellation design approach. Then a hybrid “4GEO+5IGSO” constellation is designed using the proposed approach, and the coverage performance of this constellation is analyzed in detail. Simulation results show the proposed approach can realize global seamless coverage only using a small number of satellites. Furthermore, the proposed hybrid constellation meets the coverage demand only relies on ground stations inside China.

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