Modelling and Mining Complex Networks as Hypergraphs

超图最近作为复杂网络的一种有用的表示形式出现，因为它们允许捕获比图更一般的关系。对各种基于图形的工具和技术的泛化研究正在蓬勃发展，同时，新的软件包正在开发中。话虽如此，理论和工具仍未充分发展，无法在这种背景下直接解决大多数问题。超图在知识发现领域具有特别的意义，目前大多数用图建模的问题都可以更准确地用超图建模。那些在知识发现领域的人特别感兴趣的是将模块化和扩散概念推广到超图。这样的概括需要一个坚实的理论基础来发展这些概念。不幸的是，尽管超图在20世纪60年代被正式定义（在此之前，超图的各种实现被研究了很久），但一般的形式理论还没有像从业者感兴趣的应用所需要的那样成熟。这次合作的主要目标是发展形式理论，并结合具体应用的发展。鉴于世界各地不同的研究小组最近已开始在这方面取得成果，这项建议似乎特别适合作为研究方向相互交流、建立新的合作以及交流工具和技术的机会。

Hypergraphs have recently emerged as a useful representation for complex networks since they allow capturing more general relations than graphs. Research on the generalization of various graph-based tools and techniques is booming and, in parallel, new software packages are being developed. Having said that, the theory and tools are still not sufficiently developed to allow most problems to be tackled directly within this context. Hypergraphs are of particular interest in the field of knowledge discovery where most problems currently modelled as graphs would be more accurately modelled as hypergraphs. Those in the knowledge discovery field are particularly interested in the generalization of the concepts of modularity and diffusion to hypergraphs. Such generalizations require a firm theoretical base on which to develop these concepts. Unfortunately, although hypergraphs were formally defined in the 1960s (and various realizations of hypergraphs were studied long before that), the general formal theory is not as mature as required for the applications of interest to practitioners. The main goal of this collaboration is to develop formal theory, in conjunction with development of concrete applications. Given that different research groups in various parts of the world have recently started pursuing results in this direction, the proposal seems particularly well-timed as an occasion for the cross-pollination of research directions, the establishment of new collaborations and the exchange of tools and techniques.