2026-09-19T14:25:43Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/601032022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Whitman Richards.
Macindoe, Owen
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2010-12-06T16:37:10Z
2010-12-06T16:37:10Z
2010
2010
http://hdl.handle.net/1721.1/60103
679673969
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student submitted PDF version of thesis.
Includes bibliographical references (p. 107-109).
In this thesis I explore a novel representation for characterizing a graph's fine grained structure. The key idea is that this structure can be represented as a distribution of the structural features of subgraphs. I introduce a set of such structural features and use them to compute representations for a variety of graphs, demonstrating their use in qualitatively describing fine structure. I then demonstrate the utility of this representation with quantitative techniques for computing graph similarity and graph clustering. I show that similarity judged using this representation is significantly different from judgements using full graph structural measures. I find that graphs from the same class of networks, such as email correspondence graphs, can differ significantly in their fine structure across the institutions whose relations they model, but also find examples of graphs from the same institutions across different time periods that share a similar fine structure.
by Owen Macindoe.
S.M.
109 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
copyright. They may be viewed from this source for any purpose, but
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Investigating the fine grained structure of networks
Thesis
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