We investigate the problem of correlated subgraphs mining (CSM) where the goal is to identify pairs of subgraph patterns that frequently co-occur in proximity within a single graph. Correlated subgraph patterns are different from frequent subgraphs due to the flexibility in connections between constituent subgraph instances and thus, existing frequent subgraphs mining algorithms cannot be directly applied for CSM. Moreover, computing the degree of correlation between two patterns requires enumerating and finding distances between every pair of subgraph instances of both patterns - a task that is both memory-intensive as well as computationally demanding. To this end, we propose two holistic best-first exploration algorithms: CSM-E (an exact method) and CSM-A (a more efficient approximate method with near-optimal quality). To further improve efficiency, we propose a top- k pruning strategy, while to reduce memory footprint, we develop a compressed data structure called R eplica, which stores all instances of a subgraph pattern on demand. Our empirical results demonstrate that the proposed algorithms not only mine interesting correlations, but also achieve good scalability over large networks.

Classifying and counting vehicles in road traffic has numerous applications in the transportation engineering domain. However, the wide variety of vehicles (two-wheelers, three-wheelers, cars, buses, trucks etc.) plying on roads of developing regions without any lane discipline, makes vehicle classification and counting a hard problem to automate. In this paper, we use state of the art Convolutional Neural Network (CNN) based object detection models and train them for multiple vehicle classes using data from Delhi roads. We get upto 75% MAP on an 80-20 train-test split using 5562 video frames from four different locations. As robust network connectivity is scarce in developing regions for continuous video transmissions from the road to cloud servers, we also evaluate the latency, energy and hardware cost of embedded implementations of our CNN model based inferences.