NS Seminar

Date and Location

Mar 08, 2017 - 2:00pm to 3:00pm
Bldg 434, room 122

Abstract

Analyzing Social Media Relationships in Context with Discussion Graphs (presented by Binhan Xu, Computer Science)

Kiciman, E., De Choudhury, M., Counts, S., Gamon, M., & Thiesson, B. (2013). Analyzing social media relationships in context with discussion graphs.

We present discussion graphs, a hyper-graph-based representation of social media discussions that captures both the
structural features of the relationships among entities as well as the context of the discussions from which they were derived. Building on previous analyses of social media networks that focus on the strength of relationships between entities, our discussion graphs explicitly include contextual features such as who is participating in the discussions, when and where the discussions are occurring, and what else is being discussed in conjunction. There are two contributions of this work. First, we extend standard hyper-graph representations of networks to include the distribution of contexts surrounding discussions in social media networks. Second we demonstrate how this context is useful for understanding the results of common graph measures and analyses, such as network centrality and pseudo-cliques, when applied to the analysis of textual social media content. We apply our framework across several domains captured in Twitter, including the mining of peoples' statements about their locations and activities and discussions of the U.S. 2012 elections.

 

Gene Regulatory Networks Generating the Phenomena of Additivity, Dominance and Epistasis (presented by Lilla Bartko, Ecology, Evolution, and Marine Biology)

Omholt, S. W., Plahte, E., Øyehaug, L., & Xiang, K. (2000). Gene regulatory networks generating the phenomena of additivity, dominance and epistasis. Genetics, 155(2), 969-980.

We show how the phenomena of genetic dominance, overdominance, additivity, and epistasis are generic features of simple diploid gene regulatory networks. These regulatory network models are together sufficiently complex to catch most of the suggested molecular mechanisms responsible for generating dominant mutations. These include reduced gene dosage, expression or protein activity (haploinsufficiency), increased gene dosage, ectopic or temporarily altered mRNA expression, increased or constitutive protein activity, and dominant negative effects. As classical genetics regards the phenomenon of dominance to be generated by intralocus interactions, we have studied two one-locus models, one with a negative autoregulatory feedback loop, and one with a positive autoregulatory feedback loop. To include the phenomena of epistasis and downstream regulatory effects, a model of a three-locus signal transduction network is also analyzed. It is found that genetic dominance as well as overdominance may be an intra- as well as interlocus interaction phenomenon. In the latter case the dominance phenomenon is intimately connected to either feedback-mediated epistasis or downstream-mediated epistasis. It appears that in the intra- as well as the interlocus case there is considerable room for additive gene action, which may explain to some degree the predictive power of quantitative genetic theory, with its emphasis on this type of gene action. Furthermore, the results illuminate and reconcile the prevailing explanations of heterosis, and they support the old conjecture that the phenomenon of dominance may have an evolutionary explanation related to life history strategy.