Network discovery using content and homophily
- Resource Type
- Conference
- Authors
- Smith, Steven T.; Caceres, Rajmonda S.; Senne, Kenneth D.; McMahon, Molly; Greer, Timothy
- Source
- 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Acoustics, Speech and Signal Processing (ICASSP), 2017 IEEE International Conference on. :5925-5929 Mar, 2017
- Subject
- Signal Processing and Analysis
Inference algorithms
Media
Social network services
Mathematical model
Detection algorithms
Image edge detection
Algorithm design and analysis
- Language
- ISSN
- 2379-190X
A new approach for targeted graph sampling is proposed in which graph sampling and classification occur together, and content-based homophily is exploited to achieve improved classification performance. The application of network discovery of relevant content is considered using an approach that may be generalized to a broad class of vertex properties. The resulting procedure provides the initial step of a graph analytic processing chain whose performance is directly affected by the quality of graph sampling. The performance of the algorithm is measured with real network data and content observed on a social media site. Precision-Recall performance improvements of 30% are demonstrated with this dataset, compared to a baseline approach that does not exploit homophily. Because real-world graphs grow exponentially, this performance improvement may have a significant impact on graph analytic algorithms with sensitivities to the graph sampling quality.