Influence Maximization Across Partially Aligned Heterogenous Social Networks

Zhan, Qianyi; Zhang, Jiawei; Wang, Senzhang; Yu, Philip S.; Xie, Jin

doi:10.1007/978-3-319-18038-0_5

Cited by 64 publications

(27 citation statements)

References 20 publications

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“…By fusing multiple HINs, the heterogeneous information available in each network can be transferred to other aligned networks and lots of application problems on HIN, e.g., link prediction and friend recommendation [153], [151], community detection [154], information diffusion [155], will benefit from it a lot.…”

Section: G Information Fusionmentioning

confidence: 99%

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A survey of heterogeneous information network analysis

Shi

Zhang

et al. 2017

IEEE Trans. Knowl. Data Eng.

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928

457

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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. 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

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Section: G Information Fusionmentioning

confidence: 99%

“…As a result, information can reach more users and achieve broader influence across the aligned social networks. Zhan et al propose a new model to study the information diffusion process across multiple aligned networks in [155].…”

Section: G Information Fusionmentioning

confidence: 99%

A survey of heterogeneous information network analysis

Shi

Zhang

et al. 2017

IEEE Trans. Knowl. Data Eng.

Self Cite

928

457

View full text Add to dashboard Cite

show abstract

“…Besides link prediction problems, Jin and Zhang et al proposes to partition multiple large-scale social networks simultaneously in [24]. The problem of information diffusion across partially aligned networks is studied by Zhan et al in [20], where the traditional LT diffusion model is extended to the multiple heterogeneous information setting. Shi et al give a comprehensive survey about the existing works on heterogeneous information networks in [14], which includes a section talking about network information fusion works and related application problems in detail.…”

Section: Related Workmentioning

confidence: 99%

Meta Diagram Based Active Social Networks Alignment

Ren

Aggarwal

Zhang

2019

2019 IEEE 35th International Conference on Data Engineering (ICDE)

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Network alignment aims at inferring a set of anchor links matching the shared entities between different information networks, which has become a prerequisite step for effective fusion of multiple information networks. In this paper, we will study the network alignment problem to fuse online social networks specifically. Social network alignment is extremely challenging to address due to several reasons, i.e., lack of training data, network heterogeneity and one-to-one constraint. Existing network alignment works usually require a large number of training instances, but such a demand can hardly be met in applications, as manual anchor link labeling is extremely expensive. Significantly different from other homogeneous network alignment works, information in online social networks is usually of heterogeneous categories, the incorporation of which in model building is not an easy task. Furthermore, the one-to-one cardinality constraint on anchor links renders their inference process intertwistingly correlated. To resolve these three challenges, a novel network alignment model, namely ActiveIter(Active Iterative Alignment), is introduced in this paper. The model ActiveIter defines a set of inter-network meta diagrams for anchor link feature extraction, adopts active learning for effective label query and uses greedy link selection for anchor link cardinality filtering. Extensive experiments were performed on a real-world aligned networks dataset, and the experimental results have demonstrated the effectiveness of ActiveIter compared with other state-of-the-art baseline methods.

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“…Due to the complexity of HINs, different researchers may focus on different aspects of the HINs, and thus the similarity measure between the same pair of items can be various . Therefore, researchers also proposed to cluster HINs under the guidance provided by the users (Sun et al, 2013;Zhan et al, 2015). Sun et al (Sun et al, 2017) studied how to leverage the rich semantic meaning of structural types of objects and links in HINs, and developed a structural analysis approach on mining semi-structured, multi-typed heterogeneous information networks.…”

Section: Related Workmentioning

confidence: 99%

Mutual clustering on comparative texts via heterogeneous information networks

et al. 2019

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Currently, many intelligence systems contain the texts from multi-sources, e.g., bulletin board system (BBS) posts, tweets and news. These texts can be "comparative" since they may be semantically correlated and thus provide us with different perspectives toward the same topics or events. To better organize the multi-sourced texts and obtain more comprehensive knowledge, we propose to study the novel problem of Mutual Clustering on Comparative Texts (MCCT), which aims to cluster the comparative texts simultaneously and collaboratively. The MCCT problem is difficult to address because 1) comparative texts usually present different data formats and structures and thus they are hard to organize, and 2) there lacks an effective method to connect the semantically correlated comparative texts to facilitate clustering them in an unified way. To this aim, in this paper we propose a Heterogeneous Information Network-based Text clustering framework HINT. HINT first models multi-sourced texts (e.g. news and tweets) as heterogeneous information networks by introducing the shared "anchor texts" to connect the comparative texts. Next, two similarity matrices based on HINT as well as a transition matrix for cross-text-source knowledge transfer are constructed. Comparative texts clustering are then conducted by utilizing the constructed matrices. Finally, a mutual clustering algorithm is also proposed to further unify the separate clustering results of the comparative texts by introducing a clustering consistency constraint. We conduct extensive experimental on three tweets-news datasets, 2 SZ. Wang et al and the results demonstrate the effectiveness and robustness of the proposed method in addressing the MCCT problem.

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Influence Maximization Across Partially Aligned Heterogenous Social Networks

Cited by 64 publications

References 20 publications

A survey of heterogeneous information network analysis

A survey of heterogeneous information network analysis

Meta Diagram Based Active Social Networks Alignment

Mutual clustering on comparative texts via heterogeneous information networks

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