Analyzing the harmonic structure in graph-based learning

Xiaoming Wu, Zhenguo Li, Shih Fu Chang

Research output: Journal article publicationConference articleAcademic researchpeer-review

8 Citations (Scopus)


We find that various well-known graph-basedmodels exhibit a common important harmonic structure in its target function-the value of a vertex is approximately the weighted average of the values of its adjacent neighbors. Understanding of such structure and analysis of the loss defined over such structure help reveal important properties of the target function over a graph. In this paper, we show that the variation of the target function across a cut can be upper and lower bounded by the ratio of its harmonic loss and the cut cost. We use this to develop an analytical tool and analyze five popular graph-based models: absorbing random walks, partially absorbing random walks, hitting times, pseudo-inverse of the graph Laplacian, and eigenvectors of the Laplacian matrices. Our analysis sheds new insights into several open questions related to these models, and provides theoretical justifications and guidelines for their practical use. Simulations on synthetic and real datasets confirm the potential of the proposed theory and tool.
Original languageEnglish
JournalAdvances in Neural Information Processing Systems
Publication statusPublished - 1 Jan 2013
Externally publishedYes
Event27th Annual Conference on Neural Information Processing Systems, NIPS 2013 - Lake Tahoe, NV, United States
Duration: 5 Dec 201310 Dec 2013

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing


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