Abstract:
Many realistic mobile social networks have evolving bipartite graphs, in which dynamically added elements are divided into two entities and connected by links between them, such as users and items in recommendation networks, authors and scientific topics in scholarly networks, male and female in dating social networks, etc.
However, how to mathematically model weighted evolving bipartite relationships and quantitative characterizations is unknown. Motivated by this, we develop a novel evolving bipartite model (EBM) that, based on empirically validated power-law distribution on multiple realistic mobile social networks, shows that the distribution of total weights of incoming and outgoing edges in networks is determined by the weighting scale and bounded by ceilings and floors.
EBM can predict vertice weights for evolving bipartite networks with power-law degrees based on these theoretical results. In recommendation networks, the evaluation of items, i.e., total rating scores, can be estimated through the given bounds; in scholarly networks, the total number of publications under specific topics can be predicted within a certain range; and in dating social networks, male/female favorability can be estimated.
Finally, we perform extensive experiments on 10 realistic datasets and a synthetic network with varying weights, i.e., rating scales, to further evaluate EBM. Experimental results show that given weighting scales, the EBM can correctly predict both the upper bound and the lower bound of total weights of vertices in mobile social networks.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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