Abstract
The overwhelming usage of social and collaboration networks provides the opportunity to analyze evolution of relationships among individuals, like celebrities or co-authors. Discovering such phenomenon in large complex networks is non-trivial due to their large sizes. In this situation, the aggregation functions used in OLAP, are useful to analyze the summarized data. OLAP has successfully proven its worth on multidimensional or complex networks. However, existing aggregations in the current OLAP systems do not produce versatile results in case of social and collaboration networks. This happens because said type of networks have structural connectivity/links among nodes, which cannot be considered by OLAP during its execution. In this situation, a useful discovery in terms of identifying pairs of nodes whose relationships is emerging in recent time, is missed. Such discovery of pairs of nodes is important for various applications such as targeted marketing, future joint partnerships and predicting future correspondence to name a few. In this study, we call such pairs as Rising_Pairs and propose an aggregation function for performing OLAP on network data whose historical information is maintained over a period of times. Using structural information, Rising_Pairs, our proposed aggregation function, discovers the strongly coupled pairs in a network data by emphasizing their recent interactions and attribute similarities. In this way, useful information related to strongly coupled pairs in a network is identified. To verify the effectiveness of our proposal, we implemented it on various types of real-world networks like Facebook, Digital Bibliography and Library Project (DBLP) and Global Positioning System (GPS) trajectory datasets and observed interesting patterns.
Highlights
Online social networks are extremely popular these days
We propose an aggregation function, Rising_Pairs, for performing OLAP on network datasets that estimates the strength of relationship between the pair of nodes over a given period of time
As OLAP is good at multidimensional data analysis, so vertices must be attached with multiple attributes/dimensions
Summary
People are making excessive use of them for wide variety of purposes such as social interaction (Huberman et al, 2008), content sharing (Lange, 2007), community detection (Li and Shen, 2011), viral marketing (Subramani and Rajagopalan, 2003), news sharing (Li Zhang et al, 2004), job recruitment (Calvo-Armengol and Jackson, 2004) and many others All such tasks are performed, the number of users is increasing rapidly. The celebrities can be of type couples of movie actors and actresses or a pair of researchers collaborating together It is of keen interest of such users to see how the individual relationships and the personal interests of some of the celebrities, are changing with the passage of time.
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