Finding Modes by Probabilistic Hypergraphs Shifting
  In this paper, we develop a novel paradigm, namely hypergraph shift, to find
robust graph modes by probabilistic voting strategy, which are semantically
sound besides the self-cohesiveness requirement in forming graph modes. Unlike
the existing techniques to seek graph modes by shifting vertices based on
pair-wise edges (i.e, an edge with $2$ ends), our paradigm is based on shifting
high-order edges (hyperedges) to deliver graph modes. Specifically, we convert
the problem of seeking graph modes as the problem of seeking maximizers of a
novel objective function with the aim to generate good graph modes based on
sifting edges in hypergraphs. As a result, the generated graph modes based on
dense subhypergraphs may more accurately capture the object semantics besides
the self-cohesiveness requirement. We also formally prove that our technique is
always convergent. Extensive empirical studies on synthetic and real world data
sets are conducted on clustering and graph matching. They demonstrate that our
techniques significantly outperform the existing techniques.
