Showing papers by "Srikanta Bedathur published in 2015"

Inferring and Exploiting Categories for Next Location Prediction

Abstract: Predicting the next location of a user based on their previous visiting pattern is one of the primary tasks over data from location based social networks (LBSNs) such as Foursquare. Many different aspects of these so-called "check-in" profiles of a user have been made use of in this task, including spatial and temporal information of check-ins as well as the social network information of the user. Building more sophisticated prediction models by enriching these check-in data by combining them with information from other sources is challenging due to the limited data that these LBSNs expose due to privacy concerns. In this paper, we propose a framework to use the location data from LBSNs, combine it with the data from maps for associating a set of venue categories with these locations. For example, if the user is found to be checking in at a mall that has cafes, cinemas and restaurants according to the map, all these information is associated. This category information is then leveraged to predict the next checkin location by the user. Our experiments with publicly available check-in dataset show that this approach improves on the state-of-the-art methods for location prediction.

Tracking the conductance of rapidly evolving topic-subgraphs

Abstract: Monitoring the formation and evolution of communities in large online social networks such as Twitter is an important problem that has generated considerable interest in both industry and academia. Fundamentally, the problem can be cast as studying evolving sugraphs (each subgraph corresponding to a topical community) on an underlying social graph - with users as nodes and the connection between them as edges. A key metric of interest in this setting is tracking the changes to the conductance of subgraphs induced by edge activations. This metric quantifies how well or poorly connected a subgraph is to the rest of the graph relative to its internal connections. Conductance has been demonstrated to be of great use in many applications, such as identifying bursty topics, tracking the spread of rumors, and so on. However, tracking this simple metric presents a considerable scalability challenge - the underlying social network is large, the number of communities that are active at any moment is large, the rate at which these communities evolve is high, and moreover, we need to track conductance in real-time. We address these challenges in this paper.We propose an in-memory approximation called BloomGraphs to store and update these (possibly overlapping) evolving subgraphs. As the name suggests, we use Bloom filters to represent an approximation of the underlying graph. This representation is compact and computationally efficient to maintain in the presence of updates. This is especially important when we need to simultaneously maintain thousands of evolving subgraphs. BloomGraphs are used in computing and tracking conductance of these subgraphs as edge-activations arrive. BloomGraphs have several desirable properties in the context of this application, including a small memory footprint and efficient updateability. We also demonstrate mathematically that the error incurred in computing conductance is one-sided and that in the case of evolving subgraphs the change in approximate conductance has the same sign as the change in exact conductance in most cases. We validate the effectiveness of BloomGraphs through extensive experimentation on large Twitter graphs and other social networks.

Label constrained shortest path estimation on large graphs

Abstract: In applications arising in massive on-line social networks, biological networks, and knowledge graphs it is often required to find shortest length path between two given nodes. Recent results have addressed the problem of computing either exact or good approximate shortest path distances efficiently. Some of these techniques also return the path corresponding to the estimated shortest path distance fast. Many of the real-world graphs are edge-labeled graphs, i.e., each edge has a label that denotes the relationship between the two vertices connected by the edge. However, none of the techniques for estimating shortest paths work very well when we have additional constraints on the labels associated with edges that constitute the path. In this work, we define the problem of retrieving shortest length path between two given nodes which also satisfies user-provided constraints on the set of edge labels involved in the path. We have developed SkIt index structure, which supports a wide range of label constraints on paths, and returns an accurate estimation of the shortest path that satisfies the constraints. We have conducted experiments over graphs such as social networks, and knowledge graphs that contain millions of nodes/edges, and show that SkIt index is fast, accurate in the estimated distance and has a high recall for paths that satisfy the constraints.