Temporal and multi-source fusion for detection of innovation in collaboration networks
|Title||Temporal and multi-source fusion for detection of innovation in collaboration networks|
|Publication Type||Conference Paper|
|Year of Publication||2015|
|Authors||Miller, B. A., Beard, M. S., Laubichler, M. D., and Bliss, N. T.|
|Conference Name||2015 18th International Conference on Information Fusion (Fusion)|
|Keywords||adjacency matrix, Biology, collaboration networks, Computational modeling, Eigenvalues and eigenfunctions, evolutionary biology, filtering theory, Image edge detection, matrix algebra, multisource fusion, Noise, sensor fusion, signal processing, small subgraphs, Technological innovation, temporal integration filters, Tensile stress, tensor decomposition method, tensors|
A common problem in network analysis is detecting small subgraphs of interest within a large background graph. This includes multi-source fusion scenarios where data from several modalities must be integrated to form the network. This paper presents an application of novel techniques leveraging the signal processing for graphs algorithmic framework, to well-studied collaboration networks in the field of evolutionary biology. Our multi-disciplinary approach allows us to leverage case studies of transformative periods in this scientific field as truth. We build on previous work by optimizing the temporal integration filters with respect to truth data using a tensor decomposition method that maximizes the spectral norm of the integrated subgraph's adjacency matrix. We also demonstrate that we can mitigate data corruption via fusion of different data sources, demonstrating the power of this analysis framework for incomplete and corrupted data.