TY - JOUR

T1 - Like-minded communities

T2 - bringing the familiarity and similarity together

AU - Modani, Natwar

AU - Nagar, Seema

AU - Shannigrahi, Saswata

AU - Gupta, Ritesh

AU - Dey, Kuntal

AU - Goyal, Saurabh

AU - Nanavati, Amit A.

PY - 2014/9/1

Y1 - 2014/9/1

N2 - Community detection in social networks is a well-studied problem. A community in social network is commonly defined as a group of people whose interactions within the group are more than outside the group. It is believed that people’s behavior can be linked to the behavior of their social neighborhood. While shared characteristics of communities have been used to validate the communities found, to the best of authors’ knowledge, it is not demonstrated in the literature that communities found using social interaction data are like-minded, i.e., they behave similarly in terms of their interest in items (e.g., movie, products). In this paper, we experimentally demonstrate, on a social networking movie rating dataset, that people who are interested in an item are socially better connected than the overall graph. Motivated by this fact, we propose a method for finding communities wherein like-mindedness is an explicit objective. We find small tight groups with many shared interests using a frequent item set mining approach and use these as building blocks for the core of these like-minded communities. We show that these communities have higher similarity in their interests compared to communities found using only the interaction information. We also compare our method against a baseline where the weight of edges are defined based on similarity in interests between nodes and show that our approach achieves far higher level of like-mindedness amongst the communities compared to this baseline as well.

AB - Community detection in social networks is a well-studied problem. A community in social network is commonly defined as a group of people whose interactions within the group are more than outside the group. It is believed that people’s behavior can be linked to the behavior of their social neighborhood. While shared characteristics of communities have been used to validate the communities found, to the best of authors’ knowledge, it is not demonstrated in the literature that communities found using social interaction data are like-minded, i.e., they behave similarly in terms of their interest in items (e.g., movie, products). In this paper, we experimentally demonstrate, on a social networking movie rating dataset, that people who are interested in an item are socially better connected than the overall graph. Motivated by this fact, we propose a method for finding communities wherein like-mindedness is an explicit objective. We find small tight groups with many shared interests using a frequent item set mining approach and use these as building blocks for the core of these like-minded communities. We show that these communities have higher similarity in their interests compared to communities found using only the interaction information. We also compare our method against a baseline where the weight of edges are defined based on similarity in interests between nodes and show that our approach achieves far higher level of like-mindedness amongst the communities compared to this baseline as well.

KW - Community finding

KW - Social network analysis

UR - http://www.scopus.com/inward/record.url?scp=84927805960&partnerID=8YFLogxK

U2 - 10.1007/s11280-013-0261-1

DO - 10.1007/s11280-013-0261-1

M3 - Article

AN - SCOPUS:84927805960

VL - 17

SP - 899

EP - 919

JO - World Wide Web

JF - World Wide Web

SN - 1386-145X

IS - 5

ER -