Size agnostic change points detection framework for random evolving networks

A framework for cascade size calculations on random networks

We present a framework to calculate the cascade size evolution for a large class of cascade models on random network ensembles in the limit of infinite network size. Our method is exact and applies to network ensembles with almost arbitrary degree distribution, degreedegree correlations and, in case of threshold models, for arbitrary threshold distribution. With our approach, we shift the persp...

Content Agnostic Malware Detection in Networks

Bots are the root cause of many security problems on the Internet – they send spam, steal information from infected machines, and perform distributed denial of service attacks. Given their security impact, it is not surprising that a large number of techniques have been proposed that aim to detect and mitigate bots, both network-based and host-based approaches. Detecting bots at the network-lev...

Cooperative Co-Evolving Neural Networks for Robosoccer Simulation

Critical Points for Random Boolean Networks

A model of cellular metabolism due to S. Kauffman is analyzed. It consists of a network of Boolean gates randomly assembled according to a probability distribution. It is shown that the behavior of the network depends very critically on certain simple algebraic parameters of the distribution. In some cases, the analytic results support conclusions based on simulations of random Boolean networks...

Evolving networks: Eras and turning points

Within the large body of research in complex network analysis, an important topic is the temporal evolution of networks. Existing approaches aim at analyzing the evolution on the global and the local scale, extracting properties of either the entire network or local patterns. In this paper, we focus on detecting clusters of temporal snapshots of a network, to be interpreted as eras of evolution...