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Semi-supervised learning makes the realistic assumptions that labelled data is typically rare, and that unlabelled data that are similar are likely to belong to the same class. Unlabelled data are assigned the labels associated with their “most similar” labelled neighbors. For graph-based semi-supervised learning, “most similar” is defined by weighted multipath path length in a graph. When classes...
This paper amalgamates the field of positive psychology and social network analysis to explore what are the character strengths and virtues of individuals with high centrality measures within a close group of adolescent females. Research in the field of social network analysis in the last few decades has given a good understanding of different centrality measures. Today, we know what does an individual...
In many parts of the world there are complex, violent interactions among groups with widely varying agendas. Situational awareness is difficult because there is rarely a clear distinction between good and bad actors, and there are constantly shifting alliances and oppositions between groups. This makes it difficult for analysts to understand the ecosystem of a country and region; still more to conceive...
Spectral partitioning (clustering) algorithms use eigenvectors to solve network analysis problems. The relationship between numerical accuracy and network mining quality is insufficiently understood. We show that analyzing numerical accuracy and network mining quality together leads to an algorithmic improvement. Specifically, we study spectral partitioning using sweep cuts of approximate eigenvectors...
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