Publications

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We present a detailed study on data collection, graph construction, and sampling in Twitter. We observe that sampling on semantic graphs (i.e., graphs with multiple edge types) presents fundamentally distinct challenges from sampling on traditional graphs. The purpose of our work is to present new challenges and initial solutions for sampling semantic graphs. Novel elements of our work include the following: (1) We provide a thorough discussion of problems encountered with naïve breadth-first search on semantic graphs. We argue that common sampling methods such as breadth-first search face specific challenges on semantic graphs that are not encountered on graphs with homogeneous edge types. (2) We present two competing methods for creating semantic graphs from data collects, corresponding to the interactions between sampling of different edge types. (3) We discuss new metrics specific to graphs with multiple edge types, and discuss the effect of the sampling method on these metrics. (4) We discuss issues and potential solutions pertaining to sampling semantic graphs.

The Data Inferencing on Semantic Graphs project (DISeG) was a two-year investigation of inferencing techniques (focusing on belief propagation) to social graphs with a focus on semantic graphs (also called multi-layer graphs). While working this problem, we developed a new directed version of inferencing we call Directed Propagation (Chapters 2 and 4), identified new semantic graph sampling problems (Chapter 3).

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