Difference between revisions of "Leman Akoglu et al KDD'10"
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The authors considered tie attributes and node attributes in the analysis. For tie attributes, they considered reciprocity and topology overlap. For node attributes, they considered node degrees, Cluster coefficients and user reciprocity defined as the proportion of reciprocity ties. | The authors considered tie attributes and node attributes in the analysis. For tie attributes, they considered reciprocity and topology overlap. For node attributes, they considered node degrees, Cluster coefficients and user reciprocity defined as the proportion of reciprocity ties. | ||
| − | + | We first talk about general network property of the data. The authors found that the weights on mutual node pairs in Mobile Call Graph or Mobile Text graph are both small and even. They also found that nodes with high degree tend to connect to other high-degree nodes; node strength grows in a power law manner comparing to the node degree. | |
| − | We first talk about | ||
Revision as of 22:02, 15 February 2011
Citation
Leman Akoglu, Bhavana Dalvi Structure, Tie Persistence and Event Detection in Large Phone and SMS Networks In KDD'10
Summary
This paper focuses on the same phone call data that we are going to analyze. The paper tries to answer three questions: First, what is the structural property of the dataset? What are the relationships between different quantities taken from the egonet? Second, if a link exists between two people, will the link still exist in the future? Third, how to detect change-point anomalies?
Brief Description
The authors considered tie attributes and node attributes in the analysis. For tie attributes, they considered reciprocity and topology overlap. For node attributes, they considered node degrees, Cluster coefficients and user reciprocity defined as the proportion of reciprocity ties.
We first talk about general network property of the data. The authors found that the weights on mutual node pairs in Mobile Call Graph or Mobile Text graph are both small and even. They also found that nodes with high degree tend to connect to other high-degree nodes; node strength grows in a power law manner comparing to the node degree.
