Tina Eliassi-Rad

Learn More
Networks have become ubiquitous. Communication networks, financial transaction networks, networks describing physical systems, and social networks are all becoming increasingly important in our day-to-day life. Often, we are interested in models of how objects in the network influence each other (e.g., who infects whom in an epidemiological network), or we(More)
Given a network, intuitively two nodes belong to the same role if they have similar structural behavior. Roles should be automatically determined from the data, and could be, for example, "clique-members," "periphery-nodes," etc. Roles enable numerous novel and useful network-mining tasks, such as sense-making, searching for similar nodes, and node(More)
We address the problem of classification in partially labeled networks (a.k.a. within-network classification) where observed class labels are sparse. Techniques for statistical relational learning have been shown to perform well on network classification tasks by exploiting dependencies between class labels of neighboring nodes. However, relational(More)
Given a graph, how can we extract good features for the nodes? For example, given two large graphs from the same domain, how can we use information in one to do classification in the other (i.e., perform across-network classification or transfer learning on graphs)? Also, if one of the graphs is anonymized, how can we use information in one to de-anonymize(More)
We focus on large graphs where nodes have attributes, such as a social network where the nodes are labelled with each person's job title. In such a setting, we want to find subgraphs that match a user query pattern. For example, a "star" query would be, "<i>find a CEO who has strong interactions with a Manager, a Lawyer,and an Accountant, or another(More)
Controlling the dissemination of an entity (e.g., meme, virus, etc) on a large graph is an interesting problem in many disciplines. Examples include epidemiology, computer security, marketing, etc. So far, previous studies have mostly focused on removing or inoculating <i>nodes</i> to achieve the desired outcome. We shift the problem to the level of edges(More)
Social network analysis is an active area of study beyond sociology. It uncovers the invisible relationships between actors in a network and provides understanding of social processes and behaviors. It has become an important technique in a variety of application areas such as the Web, organizational studies, and homeland security. This paper presents a(More)
Given a large graph, like a computer network, which k nodes should we immunize (or monitor, or remove), to make it as robust as possible against a computer virus attack? We need (a) a measure of the &#x02018;Vulnerability&#x02019; of a given network, b) a measure of the &#x02018;Shield-value&#x02019; of a specific set of k nodes and (c) a fast algorithm to(More)
Given a set of k networks, possibly with different sizes and no overlaps in nodes or edges, how can we quickly assess similarity between them, without solving the nodecorrespondence problem? Analogously, how can we extract a small number of descriptive, numerical features from each graph that effectively serve as the graph’s “signature”? Having such(More)
This paper introduces <i>LDA-G</i>, a scalable Bayesian approach to finding latent group structures in large real-world graph data. Existing Bayesian approaches for group discovery (such as <i>Infinite Relational Models</i>) have only been applied to small graphs with a couple of hundred nodes. LDA-G (short for <i>Latent Dirichlet Allocation for Graphs</i>)(More)