Network science provides valuable insights across numerous disciplines including sociology, biology, neuroscience and engineering. A task of major practical importance in these application domains is inferring the network topology from noisy observations over a limited subset of nodes. This work presents a novel approach for joint inference of the network topology and estimation of graph signals from partial nodal observations based on structural equation models (SEMs). SEMs have well-documented merits in identifying the directed topology of complex graphs by capturing causal relationships among nodes. The resultant algorithm iterates between inferring a directed graph that 'best' fits the data, and estimating the graph signals over the learned graph. Numerical tests with synthetic as well as real data corroborate the effectiveness of the joint inference approach.
|Original language||English (US)|
|Title of host publication||2018 IEEE Data Science Workshop, DSW 2018 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|State||Published - Aug 17 2018|
|Event||2018 IEEE Data Science Workshop, DSW 2018 - Lausanne, Switzerland|
Duration: Jun 4 2018 → Jun 6 2018
|Name||2018 IEEE Data Science Workshop, DSW 2018 - Proceedings|
|Other||2018 IEEE Data Science Workshop, DSW 2018|
|Period||6/4/18 → 6/6/18|
Bibliographical noteFunding Information:
The work in this paper was supported by NSF grant 1500713, and NIH 1R01GM104975-01.
© 2018 IEEE.
- Graph signal reconstruction
- directed graphs
- structural equation models
- topology inference