  Vast amount of multimedia data contains massive and multifarious social
information which is used to construct large-scale social networks. In a
complex social network, a character should be ideally denoted by one and only
one vertex. However, it is pervasive that a character is denoted by two or more
vertices with different names, thus it is usually considered as multiple,
different characters. This problem causes incorrectness of results in network
analysis and mining. The factual challenge is that character uniqueness is hard
to correctly confirm due to lots of complicated factors, e.g. name changing and
anonymization, leading to character duplication. Early, limited research has
shown that previous methods depended overly upon supplementary attribute
information from databases. In this paper, we propose a novel method to merge
the character vertices which refer to as the same entity but are denoted with
different names. With this method, we firstly build the relationship network
among characters based on records of social activities participated, which are
extracted from multimedia sources. Then define temporal activity paths (TAPs)
for each character over time. After that, we measure similarity of the TAPs for
any two characters. If the similarity is high enough, the two vertices should
be considered to the same character. Based on TAPs, we can determine whether to
merge the two character vertices. Our experiments shown that this solution can
accurately confirm character uniqueness in large-scale social network.
