@article{gamble_chintakunta_wilkerson_krim_2016, title={Node Dominance: Revealing Community and Core-Periphery Structure in Social Networks}, volume={2}, ISSN={["2373-776X"]}, DOI={10.1109/tsipn.2016.2527923}, abstractNote={This study relates the local property of node dominance to local and global properties of a network. Iterative removal of dominated nodes yields a distributed algorithm for computing a core-periphery decomposition of a social network, where nodes in the network core are seen to be essential in terms of network flow and global structure. Additionally, the connected components in the periphery give information about the community structure of the network, aiding in community detection. A number of explicit results are derived, relating the core and periphery to network flow, community structure, and global network structure, which are corroborated by observational results. The method is illustrated using a real world network (DBLP co-authorship network), with ground-truth communities.}, number={2}, journal={IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS}, author={Gamble, Jennifer and Chintakunta, Harish and Wilkerson, Adam and Krim, Hamid}, year={2016}, month={Jun}, pages={186–199} }
 @article{gamble_chintakunta_krim_2015, title={Coordinate-free quantification of coverage in dynamic sensor networks}, volume={114}, ISSN={["1872-7557"]}, DOI={10.1016/j.sigpro.2015.02.013}, abstractNote={We present a methodology for analyzing coverage properties in dynamic sensor networks. The dynamic sensor network under consideration is studied through a series of snapshots, and is represented by a sequence of simplicial complexes, built from the communication graph at each time point. A method from computational topology called zigzag persistent homology takes this sequence of simplicial complexes as input, and returns a 'barcode' containing the birth and death times of homological features in this sequence. We derive useful statistics from this output for analyzing time-varying coverage properties.In addition, we propose a method which returns specific representative cycles for these homological features, at each point along the birth-death intervals. These representative cycles are then used to track coverage holes in the network, and obtain size estimates for individual holes at each time point. A weighted barcode, incorporating the size information, is then used as a visual and quantitative descriptor of the dynamic network coverage. Graphical abstractDisplay Omitted HighlightsEach sensor has only a list of its neighboring sensors, with no coordinates, or inter-sensor distance information.Using these snapshots of local information, we describe the dynamic coverage properties of the network.Quantitative output is a weighted barcode, obtained using zigzag persistent homology.Estimated hole size and duration are encoded in this barcode.Method is able to distinguish between different sensor network mobility patterns.}, journal={SIGNAL PROCESSING}, author={Gamble, Jennifer and Chintakunta, Harish and Krim, Hamid}, year={2015}, month={Sep}, pages={1–18} }
 @inproceedings{gamble_chintakunta_krim_2015, title={Emergence of core-periphery structure from local node dominance in social networks}, DOI={10.1109/eusipco.2015.7362716}, abstractNote={There has been growing evidence recently for the view that social networks can be divided into a well connected core, and a sparse periphery. This paper describes how such a global description can be obtained from local "dominance" relation ships between vertices, to naturally yield a distributed algorithm for such a decomposition. It is shown that the resulting core describes the global structure of the network, while also preserving shortest paths, and displaying "expander-like" properties. Moreover, the periphery obtained from this de composition consists of a large number of connected com ponents, which can be used to identify communities in the network. These are used for a `divide-and-conquer' strategy for community detection, where the peripheral components are obtained as a pre-processing step to identify the small sets most likely to contain communities. The method is illustrated using a real world network (DBLP co-authorship network), with ground-truth communities.}, booktitle={2015 23rd european signal processing conference (eusipco)}, author={Gamble, J. and Chintakunta, H. and Krim, H.}, year={2015}, pages={1910–1914} }