text: Group centrality is an extension of the classical notion of centrality for individuals, to make it applicable to sets of them. We perform a SWOT (strengths, weaknesses, opportunities and threats) analysis of the use of group centrality in semantic networks, for different centrality notions: degree, closeness, betweenness, giving prominence to random walks. Among our main results stand out the relevance and NP-hardness of the problem of finding the most central set in a semantic network for an specific centrality measure.
  @cite_15: Many systems for tasks such as question answering, multi-document summarization, and information retrieval need robust numerical measures of lexical relatedness. Standard thesaurus-based measures of word pair similarity are based on only a single path between those words in the thesaurus graph. By contrast, we propose a new model of lexical semantic relatedness that incorporates information from every explicit or implicit path connecting the two words in the entire graph. Our model uses a random walk over nodes and edges derived from WordNet links and corpus statistics. We treat the graph as a Markov chain and compute a word-specific stationary distribution via a generalized PageRank algorithm. Semantic relatedness of a word pair is scored by a novel divergence measure, ZKL, that outperforms existing measures on certain classes of distributions. In our experiments, the resulting relatedness measure is the WordNet-based measure most highly correlated with human similarity judgments by rank ordering at = .90.
  @cite_7: 
  @cite_11: The human mind has a remarkable ability to store a vast amount of information in memory, and an even more remarkable ability to retrieve these experiences when needed. Understanding the representations and algorithms that underlie human memory search could potentially be useful in other information retrieval settings, including internet search. Psychological studies have revealed clear regularities in how people search their memory, with clusters of semantically related items tending to be retrieved together. These findings have recently been taken as evidence that human memory search is similar to animals foraging for food in patchy environments, with people making a rational decision to switch away from a cluster of related information as it becomes depleted. We demonstrate that the results that were taken as evidence for this account also emerge from a random walk on a semantic network, much like the random web surfer model used in internet search engines. This offers a simpler and more unified account of how people search their memory, postulating a single process rather than one process for exploring a cluster and one process for switching between clusters.
summary: Random Walks and Semantic Networks. The motivation to address random walk centrality comes from works that have used the notion of random walks and related it with semantics. The work of @cite_11 compares the functioning of the human mind when searching for memories with a random walk in a semantic network, as both present a similar behavior. They conclude that these results can help clarify the possible mechanisms that could account for PageRank predicting the prominence of words in semantic memory. @cite_7 use random walks to determine the semantic relatedness of two elements. In particular, they use a personalized PageRank algorithm with a custom teleport vector. They conclude that random walk combined with personalized PageRank is a feasible and potentially fruitful mean of computing semantic relatedness for words and texts. @cite_15 introduce a new measure of lexical relatedness based on the divergence of the stationary distributions computed using random walks over graphs extracted from WordNet. All these works have been valuable sources of inspiration for our method.