Mark K. Goldberg

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.

Measuring behavioral trust in social networks

Trust is an important yet complex and little understood aspect of the dyadic relationship between two entities. Trust plays an important role in the formation of coalitions in social networks and in determining how high value of information flows through the network. We present algorithmically quantifiable measures of trust based on communication behavior. We propose that trust results in likely communication behaviors which are statistically different from random communications; detecting these trust-like behaviors allows us to develop a quantitative measure of who trusts whom in the network. We develop algorithms to efficiently compute such behavioral trust and validate these measures on the Twitter network.

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.

Constructing a maximal independent set in parallel

The problem of constructing in parallel a maximal independent set of a given graph is considered. A new deterministic NC-algorithm implemented in the EREW PRAM model is presented. On graphs with n vertices and m edges, it uses

Edge-coloring of multigraphs: Recoloring technique

O((n + m)/\log n)

Construction of class 2 graphs with maximum vertex degree 3

processors and runs in

Finding Overlapping Communities in Social Networks

O(\log^3 n)

Locating hidden groups in communication networks using hidden Markov models

time. This reduces by a factor of

Measuring Similarity between Sets of Overlapping Clusters

\log n

Information cascades in social media in response to a crisis: a preliminary model and a case study

both the running time and the processor count of the previously fastest deterministic algorithm that solves the problem using a linear number of processors.