Relationship profiling over social networks: Reverse smoothness from similarity to closeness

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

On social networks, while nodes bear rich attributes, we often lack the ‘semantics’ of why each link is formed–and thus we are missing the ‘road signs’ to navigate and organize the complex social universe. How to identify relationship semantics without labeled links? Founded on the prevalent homophily principle, we propose the novel problem of Attribute-based Relationship Profiling (ARP), to profile the closeness w.r.t. the underlying relationships (e.g., schoolmate) between users based on their similarity in the corresponding attributes (e.g., schools) and, as output, learn a set of social affinity graphs, where each link is weighted by its probabilities of carrying the relationships. As requirements, ARP should be systematic and complete to profile every link for every relationship– our challenges lie in effectively modeling homophily. We propose a novel reverse smoothness principle by observing that the similarity-closeness duality of homophily is consistent with the well-known smoothness assumption in graph-based semi-supervised learning– only the direction of inference is reversed. To realize smoothness over noisy social graphs, we further propose a novel holistic closeness modeling approach to capture ‘high-order’ smoothness by extending closeness from edges to paths. Extensive experiments on three real-world datasets demonstrate the efficacy of our proposed algorithm for ARP.

Original languageEnglish (US)
Title of host publicationSIAM International Conference on Data Mining, SDM 2019
PublisherSociety for Industrial and Applied Mathematics Publications
Pages342-350
Number of pages9
ISBN (Electronic)9781611975673
StatePublished - Jan 1 2019
Event19th SIAM International Conference on Data Mining, SDM 2019 - Calgary, Canada
Duration: May 2 2019May 4 2019

Publication series

NameSIAM International Conference on Data Mining, SDM 2019

Conference

Conference19th SIAM International Conference on Data Mining, SDM 2019
CountryCanada
CityCalgary
Period5/2/195/4/19

Fingerprint

Semantics
Supervised learning
Experiments

ASJC Scopus subject areas

  • Software

Cite this

Yang, C., & Chang, K. C-C. (2019). Relationship profiling over social networks: Reverse smoothness from similarity to closeness. In SIAM International Conference on Data Mining, SDM 2019 (pp. 342-350). (SIAM International Conference on Data Mining, SDM 2019). Society for Industrial and Applied Mathematics Publications.

Relationship profiling over social networks : Reverse smoothness from similarity to closeness. / Yang, Carl; Chang, Kevin Chen-Chuan.

SIAM International Conference on Data Mining, SDM 2019. Society for Industrial and Applied Mathematics Publications, 2019. p. 342-350 (SIAM International Conference on Data Mining, SDM 2019).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Yang, C & Chang, KC-C 2019, Relationship profiling over social networks: Reverse smoothness from similarity to closeness. in SIAM International Conference on Data Mining, SDM 2019. SIAM International Conference on Data Mining, SDM 2019, Society for Industrial and Applied Mathematics Publications, pp. 342-350, 19th SIAM International Conference on Data Mining, SDM 2019, Calgary, Canada, 5/2/19.
Yang C, Chang KC-C. Relationship profiling over social networks: Reverse smoothness from similarity to closeness. In SIAM International Conference on Data Mining, SDM 2019. Society for Industrial and Applied Mathematics Publications. 2019. p. 342-350. (SIAM International Conference on Data Mining, SDM 2019).
Yang, Carl ; Chang, Kevin Chen-Chuan. / Relationship profiling over social networks : Reverse smoothness from similarity to closeness. SIAM International Conference on Data Mining, SDM 2019. Society for Industrial and Applied Mathematics Publications, 2019. pp. 342-350 (SIAM International Conference on Data Mining, SDM 2019).
@inproceedings{23ed2f5305d241208d549822ffcd6e4a,
title = "Relationship profiling over social networks: Reverse smoothness from similarity to closeness",
abstract = "On social networks, while nodes bear rich attributes, we often lack the ‘semantics’ of why each link is formed–and thus we are missing the ‘road signs’ to navigate and organize the complex social universe. How to identify relationship semantics without labeled links? Founded on the prevalent homophily principle, we propose the novel problem of Attribute-based Relationship Profiling (ARP), to profile the closeness w.r.t. the underlying relationships (e.g., schoolmate) between users based on their similarity in the corresponding attributes (e.g., schools) and, as output, learn a set of social affinity graphs, where each link is weighted by its probabilities of carrying the relationships. As requirements, ARP should be systematic and complete to profile every link for every relationship– our challenges lie in effectively modeling homophily. We propose a novel reverse smoothness principle by observing that the similarity-closeness duality of homophily is consistent with the well-known smoothness assumption in graph-based semi-supervised learning– only the direction of inference is reversed. To realize smoothness over noisy social graphs, we further propose a novel holistic closeness modeling approach to capture ‘high-order’ smoothness by extending closeness from edges to paths. Extensive experiments on three real-world datasets demonstrate the efficacy of our proposed algorithm for ARP.",
author = "Carl Yang and Chang, {Kevin Chen-Chuan}",
year = "2019",
month = "1",
day = "1",
language = "English (US)",
series = "SIAM International Conference on Data Mining, SDM 2019",
publisher = "Society for Industrial and Applied Mathematics Publications",
pages = "342--350",
booktitle = "SIAM International Conference on Data Mining, SDM 2019",
address = "United States",

}

TY - GEN

T1 - Relationship profiling over social networks

T2 - Reverse smoothness from similarity to closeness

AU - Yang, Carl

AU - Chang, Kevin Chen-Chuan

PY - 2019/1/1

Y1 - 2019/1/1

N2 - On social networks, while nodes bear rich attributes, we often lack the ‘semantics’ of why each link is formed–and thus we are missing the ‘road signs’ to navigate and organize the complex social universe. How to identify relationship semantics without labeled links? Founded on the prevalent homophily principle, we propose the novel problem of Attribute-based Relationship Profiling (ARP), to profile the closeness w.r.t. the underlying relationships (e.g., schoolmate) between users based on their similarity in the corresponding attributes (e.g., schools) and, as output, learn a set of social affinity graphs, where each link is weighted by its probabilities of carrying the relationships. As requirements, ARP should be systematic and complete to profile every link for every relationship– our challenges lie in effectively modeling homophily. We propose a novel reverse smoothness principle by observing that the similarity-closeness duality of homophily is consistent with the well-known smoothness assumption in graph-based semi-supervised learning– only the direction of inference is reversed. To realize smoothness over noisy social graphs, we further propose a novel holistic closeness modeling approach to capture ‘high-order’ smoothness by extending closeness from edges to paths. Extensive experiments on three real-world datasets demonstrate the efficacy of our proposed algorithm for ARP.

AB - On social networks, while nodes bear rich attributes, we often lack the ‘semantics’ of why each link is formed–and thus we are missing the ‘road signs’ to navigate and organize the complex social universe. How to identify relationship semantics without labeled links? Founded on the prevalent homophily principle, we propose the novel problem of Attribute-based Relationship Profiling (ARP), to profile the closeness w.r.t. the underlying relationships (e.g., schoolmate) between users based on their similarity in the corresponding attributes (e.g., schools) and, as output, learn a set of social affinity graphs, where each link is weighted by its probabilities of carrying the relationships. As requirements, ARP should be systematic and complete to profile every link for every relationship– our challenges lie in effectively modeling homophily. We propose a novel reverse smoothness principle by observing that the similarity-closeness duality of homophily is consistent with the well-known smoothness assumption in graph-based semi-supervised learning– only the direction of inference is reversed. To realize smoothness over noisy social graphs, we further propose a novel holistic closeness modeling approach to capture ‘high-order’ smoothness by extending closeness from edges to paths. Extensive experiments on three real-world datasets demonstrate the efficacy of our proposed algorithm for ARP.

UR - http://www.scopus.com/inward/record.url?scp=85066083578&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85066083578&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:85066083578

T3 - SIAM International Conference on Data Mining, SDM 2019

SP - 342

EP - 350

BT - SIAM International Conference on Data Mining, SDM 2019

PB - Society for Industrial and Applied Mathematics Publications

ER -