Xiaolin Shi will be defending her thesis on June 3rd, 2:00 – 4:00 PM, in CSE 3725 (the computer science building). She’s the first student of mine to have reached this stage, and so I’m experiencing a bit of anxiety, though she is quite ready. Next up for her is a postdoc with Dan MacFarland at Stanford (she’ll be part of an interdisciplinary team of computer scientists, linguists, and education-folk to study how the education environment impacts future scholarly performance).
Info on her thesis:
THE STRUCTURE AND DYNAMICS OF INFORMATION SHARING NETWORKS
Information flows are produced, carried, and directed by information sharing networks. And the evolution of the structure of such networks and the way information diffuses are affected by one another. This thesis studies structural features of information networks and their relationships to information diffusion…
It starts with the
robustness study of topological features when the data sets are sampled from net-
works which are rapidly evolving, as is the case in large-scale online blog networks.
The features of blog networks are found to be stable upon aggregation with compre-
hensive data sets, even as individual network ties are highly intermittent. Another
salient structural feature of such networks is that a small number of vertices play a
disproportionately important role through their position and connectivity. In sev-
eral online information networks studied in this thesis, one can construct subgraphs
of vertices according to different importance measures, while consistently preserv-
ing their attributes such as connectivity and shortest paths in the original networks.
Simple connectivity through arbitrary ties is sometimes insufficient to transmit infor-
mation, because ties may need to be of a given strength in some real-world scenarios.
In this thesis, we further show that strong ties percolate through online social net-
works, and increasing the required threshold strength does not break up the network
into isolated communities, nor lengthen the average shortest paths of the networks.
After examining the structural features which will potentially affect the flow of
information, this thesis further examines the actual relationship between structure
and information flow, and flow and impact. A thorough analysis based on citation
networks indicates that information is less likely to flow across community bound-
aries, and a publication’s citing across disciplines is tied to its subsequent impact.
In the case of patents and natural science publications, those that are cited at least
once are cited slightly more when they draw on research outside of their area. In
contrast, in the social sciences, citing within one’s own field tends to be positively
correlated with impact. This thesis also studies information diffusion in online com-
munities. The patterns of information diffusion curves reflecting user behavior in
joining groups and the feature factors associated with users or groups that influence
such behavior are studied. Bipartite Markov Random Field (BiMRF) models are
built to help understand the relationships of these features, as well as the differences
in their impact in different types of online forums.