论文标题
Spikyball采样：通过不均匀的过滤扩散探索大型网络
Spikyball sampling: Exploring large networks via an inhomogeneous filtered diffusion
论文作者
论文摘要
研究现实世界网络（例如社交网络或网络网络）是一个挑战。这些网络通常将复杂的高度连接结构与大尺寸结合在一起。我们为大型网络提供了一种新方法，该方法能够自动采样网络的用户定义的相关部分。从网络中的一些选定位置开始，该方法采用过滤的广度优先搜索方法，该方法通过与这些属性相匹配的边缘和节点进行扩展。此外，在每个步骤的邻居的随机子集上进行扩展，以进一步减轻大图中可能存在的压倒性连接数量。这带有“尖峰”扩展的图像。我们表明，这种方法概括了先前的探索方法，例如雪球或森林射击，并将其扩展。我们展示了其能够捕获具有高相互作用的节点组的能力，同时丢弃弱连接的节点，这些节点通常在社交网络中很多，并且可能隐藏了重要的结构。
Studying real-world networks such as social networks or web networks is a challenge. These networks often combine a complex, highly connected structure together with a large size. We propose a new approach for large scale networks that is able to automatically sample user-defined relevant parts of a network. Starting from a few selected places in the network and a reduced set of expansion rules, the method adopts a filtered breadth-first search approach, that expands through edges and nodes matching these properties. Moreover, the expansion is performed over a random subset of neighbors at each step to mitigate further the overwhelming number of connections that may exist in large graphs. This carries the image of a "spiky" expansion. We show that this approach generalize previous exploration sampling methods, such as Snowball or Forest Fire and extend them. We demonstrate its ability to capture groups of nodes with high interactions while discarding weakly connected nodes that are often numerous in social networks and may hide important structures.
