A survey of heterogeneous information network analysis

Abstract

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. Recently, more and more researchers begin to consider these interconnected, multi-typed data as heterogeneous information networks, and develop structural analysis approaches by leveraging the rich semantic meaning of structural types of objects and links in the networks. Compared to widely studied homogeneous network, the heterogeneous information network contains richer structure and semantic information, which provides plenty of opportunities as well as a lot of challenges for data mining. In this paper, we provide a survey of heterogeneous information network analysis. We will introduce basic concepts of heterogeneous information network analysis, examine its developments on different data mining tasks, discuss some advanced topics, and point out some future research directions. Index Termsheterogeneous information network, data mining, semi-structural data, meta path

A Hybrid Service Recommendation Prototype Adapted for the UCWW: A Smart-City Orientation

“…To further improve the recommendation performance, HINs have been used to model information related to users and items, in which entities are of various types and links represent various types of relations [21]. Yu et al [22] introduce a matrix factorization approach with entity similarity regularization, where the similarity is derived from metapaths in a HIN.…”

“…Thus, the recommendation problem could be modeled with heterogeneous information networks (HINs) [21]. The following definition of an information network was adopted from [21].…”

A Survey on Clustering Methods in Data Mining

“…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].…”

“…Performance, quality and accuracy of the algorithms are discussed with respect to all these four factors [5]. Reference [1] has given a common schematic of the architecture of participation anticipating system in presidential election by using KNN, Classification Tree and Naïve Bayes and tools orange based on crisp and by anticipating the political behavior of people in elections can determine the future prospect of each country domestic and foreign policies and characterize domestic and international relationships. Overall four opinions are considered and specified in point and those are knowledge resource; knowledge types and/or knowledge datasets; data mining tasks; and data mining tasks and applications used in data running and beside with this terms data mining functionalities are surveyed [2].…”

“…Graham Cormode has introduce their idea on the key concepts in data mining along with that he has discussed the things like as clustering problems and some of the other clustering techniques. As tremendous amount of work is being done on the clustering methods in data mining space , Among those fuzzy c-means is nothing but the famous strategy of clustering and the reference [1] suggests that the fuzzy c-means (FCM) algorithm is important tool for clustering real dimensional data, yet we cannot apply it to the incomplete data. In these clustering techniques, a soft computing technique play a extremely significant role and it is because outstanding to the reality that soft computing strategies allows the pattern to belong to more than 1 cluster.…”