Link prediction in social networks with edge aging

Ricky Laishram; Kishan Mehrotra; Chilukuri K. Mohan

doi:10.1109/ICTAI.2016.95

Link prediction in social networks with edge aging

Ricky Laishram, Kishan Mehrotra, Chilukuri K. Mohan

Department of Electrical Engineering & Computer Science

Research output: Chapter in Book/Entry/Poem › Conference contribution

6 Scopus citations

Abstract

In social networks that change with time, an important problem is the prediction of new links that may be formed in the future. Existing works on link prediction have focused only on networks where links are permanent, an assumption that is not valid in many real world social networks. In many real world networks, in addition to new links being created, existing links also get removed. In this paper, we extend existing link prediction methods and apply a supervised learning algorithm to networks with non-permanent links. The results we obtain on Twitter @-mention networks show that our method performs very well in such networks.

Original language	English (US)
Title of host publication	Proceedings - 2016 IEEE 28th International Conference on Tools with Artificial Intelligence, ICTAI 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	606-613
Number of pages	8
ISBN (Electronic)	9781509044597
DOIs	https://doi.org/10.1109/ICTAI.2016.95
State	Published - Jan 11 2017
Event	28th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2016 - San Jose, United States Duration: Nov 6 2016 → Nov 8 2016

Other

Other	28th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2016
Country/Territory	United States
City	San Jose
Period	11/6/16 → 11/8/16

Keywords

Artificial intelligence
Link prediction
Machine learning
Social network

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications

Access to Document

10.1109/ICTAI.2016.95

Cite this

Laishram, R, Mehrotra, K & Mohan, CK 2017, Link prediction in social networks with edge aging. in Proceedings - 2016 IEEE 28th International Conference on Tools with Artificial Intelligence, ICTAI 2016., 7814658, Institute of Electrical and Electronics Engineers Inc., pp. 606-613, 28th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2016, San Jose, United States, 11/6/16. https://doi.org/10.1109/ICTAI.2016.95

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title = "Link prediction in social networks with edge aging",

abstract = "In social networks that change with time, an important problem is the prediction of new links that may be formed in the future. Existing works on link prediction have focused only on networks where links are permanent, an assumption that is not valid in many real world social networks. In many real world networks, in addition to new links being created, existing links also get removed. In this paper, we extend existing link prediction methods and apply a supervised learning algorithm to networks with non-permanent links. The results we obtain on Twitter @-mention networks show that our method performs very well in such networks.",

keywords = "Artificial intelligence, Link prediction, Machine learning, Social network",

author = "Ricky Laishram and Kishan Mehrotra and Mohan, {Chilukuri K.}",

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doi = "10.1109/ICTAI.2016.95",

language = "English (US)",

pages = "606--613",

booktitle = "Proceedings - 2016 IEEE 28th International Conference on Tools with Artificial Intelligence, ICTAI 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "28th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2016 ; Conference date: 06-11-2016 Through 08-11-2016",

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AU - Laishram, Ricky

AU - Mehrotra, Kishan

AU - Mohan, Chilukuri K.

PY - 2017/1/11

Y1 - 2017/1/11

N2 - In social networks that change with time, an important problem is the prediction of new links that may be formed in the future. Existing works on link prediction have focused only on networks where links are permanent, an assumption that is not valid in many real world social networks. In many real world networks, in addition to new links being created, existing links also get removed. In this paper, we extend existing link prediction methods and apply a supervised learning algorithm to networks with non-permanent links. The results we obtain on Twitter @-mention networks show that our method performs very well in such networks.

AB - In social networks that change with time, an important problem is the prediction of new links that may be formed in the future. Existing works on link prediction have focused only on networks where links are permanent, an assumption that is not valid in many real world social networks. In many real world networks, in addition to new links being created, existing links also get removed. In this paper, we extend existing link prediction methods and apply a supervised learning algorithm to networks with non-permanent links. The results we obtain on Twitter @-mention networks show that our method performs very well in such networks.

KW - Artificial intelligence

KW - Link prediction

KW - Machine learning

KW - Social network

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U2 - 10.1109/ICTAI.2016.95

DO - 10.1109/ICTAI.2016.95

M3 - Conference contribution

AN - SCOPUS:85013657130

SP - 606

EP - 613

BT - Proceedings - 2016 IEEE 28th International Conference on Tools with Artificial Intelligence, ICTAI 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 28th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2016

Y2 - 6 November 2016 through 8 November 2016

ER -

Link prediction in social networks with edge aging

Abstract

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Keywords

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