— Given a large graph and a set of objects, the task of object connection discovery is to find a subgraph that retains the best connection between the objects. Object connection discovery is useful to many important applications such as discovering the connection between different terrorist groups for counter-terrorism operations. Existing work considers only the connection between individual objects; however, in many real problems, the objects usually have a context (e.g., a terrorist belongs to a terrorist group). We identify the context for the nodes in a large graph. We partition the graph into a set of communities based on the concept of modularity, where each community becomes naturally the context of the nodes within the community. By considering the context, we also significantly improve the efficiency of object connection discovery, since we break down the big graph into much smaller communities. We first compute the best intra-community connection by maximizing the amou...