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The notion of trends in social networks has emerged as an important problem attracting the attention of researchers as well as the industry. Although, recent work has studied trends from various perspectives such as its temporal and geospatial properties, the structural properties of the network that creates such trends are ignored in trend detection. In this paper, the authors propose two novel structural trend definitions called correlated and uncorrelated trends that leverage friendship information to detect interesting topics that would not be detected using traditional trend definitions. They experimentally and analytically show that correlated trends are significantly different from traditional trends whereas the difference for uncorrelated trends, although corresponding to a useful variation, is less pronounced.
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