Mining heterogeneous information networks

Sun, Yizhou; Han, Jiawei

doi:10.1145/2481244.2481248

Cited by 528 publications

(228 citation statements)

References 32 publications

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“…Today, it can be found that many research works have done on clustering techniques in data mining space. [1][2][3][4][5][6][7][8] offered a board view for the different clustering techniques and comparison between them considering the advantages and disadvantages of the same. Different soft computing methodologies like Fuzzy sets are generally good to handle the controversies regarding understandability of patterns, mixed media information; noisy data can provide approximate solutions faster [3].…”

Section: Related Workmentioning

confidence: 99%

“…Different soft computing methodologies like Fuzzy sets are generally good to handle the controversies regarding understandability of patterns, mixed media information; noisy data can provide approximate solutions faster [3]. Information excess problem can be resolve by biomedical text retrieval, which will facilitate the researchers, by hunting the analytical relationships among different pieces of extracted data, and after that we can relate the data mining algorithm to increase the effectiveness of the information retrieval system or the process [4,15]. The various algorithms are compared on the root of factors like as size of datasets, elements of cluster, variety of dataset and variety of software used.…”

Section: Related Workmentioning

confidence: 99%

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A Survey on Clustering Methods in Data Mining

Pathak¹,

Lal²

2017

IJCA

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With increasing amount of data in information industry, there may be an immense amount from claiming information accessible in the majority of the business data. This information will be of no utilization until it is changed over under suitable data. It found important to examine this immense amount of data and withdrawal meaningful knowledge from it. Data mining is an important methodology in withdrawal of meaningful knowledge from large cluster of data. Clustering is included in the tasks of data mining. Clustering is one of the task in which making a group of physical objects into classes of similar objects. In this review paper, we give a study of various clustering methods in data mining for information retrieval and other purposes. We will describe fundamental study of clustering and will analyze each methodology by doing comparative study in table format and examine the clustering algorithms for heterogeneous data.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Survey on Clustering Methods in Data Mining

Pathak¹,

Lal²

2017

IJCA

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“…Heterogeneous network [2]. An information network is defined as a directed graph Because the heterogeneous network has different types of objects or relationships, it has the characteristics of multi-mode and multi-dimensional.…”

Section: Preliminary Heterogeneous Networkmentioning

confidence: 99%

“…However, most of the contemporary community detection analyses have a basic assumption: the type of the objects or links is unique [2]. In other words, the modeled networks are homogeneous networks containing the same type of objects and links.…”

Section: Introductionmentioning

confidence: 99%

“…But most real systems contain multi-typed interacting components. For example, in the bibliographic database, like DBLP [2], papers are connected through authors, venues and terms. Although homogeneous network provides a convenient method for network analysis, it ignores the heterogeneity of objects and links or only considers one type of relations among one type of objects.…”

Section: Introductionmentioning

confidence: 99%

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Community Detection of Heterogeneous Networks with RankClus Based on Spark GraphX

Yang¹,

Guo²,

Deng³

et al. 2017

dtcse

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Most real systems consist of a large number of interacting, multi-typed components, while most contemporary researches model them as homogeneous networks without distinguishing different types of objects and links in the networks. Compared with the homogeneous networks, community detection based on the heterogeneous networks could obtain more accurate community structure. Most of contemporary community detection tasks only work on small dataset and fail to consider the quick and parallel process on big data. In this paper, we propose a method to detect communities in heterogeneous networks and solve the parallelisim problem on big networked data. Firstly, we improve and optimize the RankClus algorithm for community detection in heterogeneous networks. Then, we parallelize the improved algorithm based on spark graphx. Finally, we present the experiments of the parallel algorithm and compare the different experiment results which demenstrates that our method is applicable and has efficient results for community detection of heterogeneous networks.

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“Heterogeneous couplings”: Operationalizing network perspectives to study science‐society interactions through social media metrics

Costas

Rijcke

Marres

2020

Asso for Info Science & Tech

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Social media metrics have a genuine networked nature, reflecting the networking characteristics of the social media platform from where they are derived. This networked nature has been relatively less explored in the literature on altmetrics, although new network-level approaches are starting to appear. A general conceptualization of the role of social media networks in science communication, and particularly of social media as a specific type of interface between science and society, is still missing. The aim of this paper is to provide a conceptual framework for appraising interactions between science and society in multiple directions, in what we call heterogeneous couplings. Heterogeneous couplings are conceptualized as the co-occurrence of science and nonscience objects, actors, and interactions in online media environments. This conceptualization provides a common framework to study the interactions between science and non-science actors as captured via online and social media platforms. The conceptualization of heterogeneous couplings opens wider opportunities for the development of network applications and analyses of the interactions between societal and scholarly entities in social media environments, paving the way toward more advanced forms of altmetrics, social (media) studies of science, and the conceptualization and operationalization of more advanced science-society studies.

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Mining heterogeneous information networks

Cited by 528 publications

References 32 publications

A Survey on Clustering Methods in Data Mining

A Survey on Clustering Methods in Data Mining

Community Detection of Heterogeneous Networks with RankClus Based on Spark GraphX

“Heterogeneous couplings”: Operationalizing network perspectives to study science‐society interactions through social media metrics

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