Location Based Social Network For Rating Procedure Geographical Location Using Extended Collaborative Algorithm

Modern, Internet-based social networks contain a wealth of information about each member. An integral part of an individual’s online profile is their Volunteered Geographic Information (VGI) such as a user’s current geographical location. Social network members in different cities, countries, or continents engage in different activities due to accessibility, economy, culture, or other factors. The work here focuses on data mining separate groups of social network profiles according to their geography in order to discover information about a place. This results in keywords associated with a specific location and provides an automated way to describe a place in an up to date fashion based upon its current local residents. Location-Based Social Network (LBSN) profiles from four different places are analyzed here and the results are presented as they relate to space, time, and activities.

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Data Mining Location-Based Social Networks for Geospatial Discovery

Discovery of Geospatial Resources ◽

10.4018/978-1-4666-0945-7.ch010 ◽

2012 ◽

pp. 204-218

Author(s):

Edward Pultar

Keyword(s):

Data Mining ◽

Social Networks ◽

Social Network ◽

Geographical Location ◽

Geographic Information ◽

Space Time ◽

Specific Location ◽

Local Residents ◽

Location Based Social Network ◽

Location Based Social Networks

Modern, Internet-based social networks contain a wealth of information about each member. An integral part of an individual’s online profile is their Volunteered Geographic Information (VGI) such as a user’s current geographical location. Social network members in different cities, countries, or continents engage in different activities due to accessibility, economy, culture, or other factors. The work here focuses on data mining separate groups of social network profiles according to their geography in order to discover information about a place. This results in keywords associated with a specific location and provides an automated way to describe a place in an up to date fashion based upon its current local residents. Location-Based Social Network (LBSN) profiles from four different places are analyzed here and the results are presented as they relate to space, time, and activities.

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A study and analysis of recommendation systems for location-based social network (LBSN) with big data

IIMB Management Review ◽

10.1016/j.iimb.2016.01.001 ◽

2016 ◽

Vol 28 (1) ◽

pp. 25-30 ◽

Cited By ~ 12

Author(s):

Murale Narayanan ◽

Aswani Kumar Cherukuri

Keyword(s):

Big Data ◽

Social Network ◽

Recommendation Systems ◽

Location Based Social Network

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A partial knowledge of friends of friends speeds social search

PLoS ONE ◽

10.1371/journal.pone.0255982 ◽

2021 ◽

Vol 16 (8) ◽

pp. e0255982

Author(s):

Amr Elsisy ◽

Boleslaw K. Szymanski ◽

Jasmine A. Plum ◽

Miao Qi ◽

Alex Pentland

Keyword(s):

Social Network ◽

Human Mobility ◽

Sufficient Conditions ◽

Partial Knowledge ◽

Social Search ◽

Strongly Nonlinear ◽

Original Distribution ◽

Personal Connections ◽

Location Based Social Network ◽

Necessary And Sufficient

Milgram empirically showed that people knowing only connections to their friends could locate any person in the U.S. in a few steps. Later research showed that social network topology enables a node aware of its full routing to find an arbitrary target in even fewer steps. Yet, the success of people in forwarding efficiently knowing only personal connections is still not fully explained. To study this problem, we emulate it on a real location-based social network, Gowalla. It provides explicit information about friends and temporal locations of each user useful for studies of human mobility. Here, we use it to conduct a massive computational experiment to establish new necessary and sufficient conditions for achieving social search efficiency. The results demonstrate that only the distribution of friendship edges and the partial knowledge of friends of friends are essential and sufficient for the efficiency of social search. Surprisingly, the efficiency of the search using the original distribution of friendship edges is not dependent on how the nodes are distributed into space. Moreover, the effect of using a limited knowledge that each node possesses about friends of its friends is strongly nonlinear. We show that gains of such use grow statistically significantly only when this knowledge is limited to a small fraction of friends of friends.

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