Jeffrey Baumes

Efficient identification of overlapping communities

In this paper, we present an efficient algorithm for finding overlapping communities in social networks. Our algorithm does not rely on the contents of the messages and uses the communication graph only. The knowledge of the structure of the communities is important for the analysis of social behavior and evolution of the society as a whole, as well as its individual members. This knowledge can be helpful in discovering groups of actors that hide their communications, possibly for malicious reasons. Although the idea of using communication graphs for identifying clusters of actors is not new, most of the traditional approaches, with the exception of the work by Baumes et al, produce disjoint clusters of actors, de facto postulating that an actor is allowed to belong to at most one cluster. Our algorithm is significantly more efficient than the previous algorithm by Baumes et al; it also produces clusters of a comparable or better quality.

Discovering Hidden Groups in Communication Networks

We describe models and efficient algorithms for detecting groups (communities) functioning in communication networks which attempt to hide their functionality – hidden groups. Our results reveal the properties of the background network activity that make detection of the hidden group easy, as well as those that make it difficult.

Finding hidden group structure in a stream of communications

A hidden group in a communication network is a group of individuals planning an activity over a communication medium without announcing their intentions. We develop algorithms for separating non-random planning-related communications from random background communications in a streaming model. This work extends previous results related to the identification of hidden groups in the cyclic model. The new statistical model and new algorithms do not assume the existence of a planning time-cycle in the stream of communications of a hidden group. The algorithms construct larger hidden groups by building them up from smaller ones. To illustrate our algorithms, we apply them to the Enron email corpus in order to extract the evolution of Enron’s organizational structure.

A new Gödelian argument for hypercomputing minds based on the busy beaver problem

Abstract Do human persons hypercompute? Or, as the doctrine of computationalism holds, are they information processors at or below the Turing Limit? If the former, given the essence of hypercomputation, persons must in some real way be capable of infinitary information processing. Using as a springboard Godel’s little-known assertion that the human mind has a power “converging to infinity”, and as an anchoring problem Rado’s [T. Rado, On non-computable functions, Bell System Technical Journal 41 (1963) 877–884] Turing-uncomputable “busy beaver” (or Σ ) function, we present in this short paper a new argument that, in fact, human persons can hypercompute. The argument is intended to be formidable, not conclusive: it brings Godel’s intuition to a greater level of precision, and places it within a sensible case against computationalism.

ViSAGE: A Vi rtual Laboratory for S imulation and A nalysis of Social G roup E volution

We present a modeling laboratory, Virtual Laboratory for the Simulation and Analysis of Social Group Evolution (ViSAGE), that views the organization of human communities and the experience of individuals in a community as contingent upon on the dynamic properties, or micro-laws, of social groups. The laboratory facilitates the theorization and validation of these properties through an iterative research processes that involves (1) forward simulation experiments, which are used to formalize dynamic group properties, (2) reverse engineering from real data on how the parameters are distributed among individual actors in the community, and (3) grounded research, such as participant observation, that follows specific activities of real actors in a community and determines if, or how well, the micro-laws describe the way choices are made in real world, local settings. In this article we report on the design of ViSAGE. We first give some background to the model. Next we detail each component. We then describe a set of simulation experiments that we used to further design and clarify ViSAGE as a tool for studying emergent properties/phenomena in social networks.