On parsimony and clustering.

This work is motivated by applications of parsimonious cladograms for the purpose of analyzing non-biological data. Parsimonious cladograms were introduced as a means to help understanding the tree of life, and are now used in fields related to biological sciences at large, e.g., to analyze viruses or to predict the structure of proteins. We revisit parsimonious cladograms through the lens of clustering and compare cladograms optimized for parsimony with dendograms obtained from single linkage hierarchical clustering. We show that despite similarities in both approaches, there exist datasets whose clustering dendogram is incompatible with parsimony optimization. Furthermore, we provide numerical examples to compare via F-scores the clustering obtained through both parsimonious cladograms and single linkage hierarchical dendograms.

The first year of the Covid-19 pandemic through the lens of r/Coronavirus subreddit: an exploratory study.

Data: This study looks at the content on Reddit's COVID-19 community, r/Coronavirus, to capture and understand the main themes and discussions around the global pandemic, and their evolution over the first year of the pandemic. It studies 356,690 submissions (posts) and 9,413,331 comments associated with the submissions, corresponding to the period of 20th January 2020 and 31st January 2021. Methodology: On each of these datasets we carried out analysis based on lexical sentiment and topics generated from unsupervised topic modelling. The study found that negative sentiments show higher ratio in submissions while negative sentiments were of the same ratio as positive ones in the comments. Terms associated more positively or negatively were identified. Upon assessment of the upvotes and downvotes, this study also uncovered contentious topics, particularly "fake" or misleading news. Results: Through topic modelling, 9 distinct topics were identified from submissions while 20 were identified from comments. Overall, this study provides a clear overview on the dominating topics and popular sentiments pertaining the pandemic during the first year. Conclusion: Our methodology provides an invaluable tool for governments and health decision makers and authorities to obtain a deeper understanding of the dominant public concerns and attitudes, which is vital for understanding, designing and implementing interventions for a global pandemic.

Renyi entropy driven hierarchical graph clustering.

This article explores a graph clustering method that is derived from an information theoretic method that clusters points in R n relying on Renyi entropy, which involves computing the usual Euclidean distance between these points. Two view points are adopted: (1) the graph to be clustered is first embedded into R d for some dimension d so as to minimize the distortion of the embedding, then the resulting points are clustered, and (2) the graph is clustered directly, using as distance the shortest path distance for undirected graphs, and a variation of the Jaccard distance for directed graphs. In both cases, a hierarchical approach is adopted, where both the initial clustering and the agglomeration steps are computed using Renyi entropy derived evaluation functions. Numerical examples are provided to support the study, showing the consistency of both approaches (evaluated in terms of F-scores).

On Grid Quorums for Erasure Coded Data.

We consider the problem of designing grid quorum systems for maximum distance separable (MDS) erasure code based distributed storage systems. Quorums are used as a mechanism to maintain consistency in replication based storage systems, for which grid quorums have been shown to produce optimal load characteristics. This motivates the study of grid quorums in the context of erasure code based distributed storage systems. We show how grid quorums can be built for erasure coded data, investigate the load characteristics of these quorum systems, and demonstrate how sequential consistency is achieved even in the presence of storage node failures.

A split-and-transfer flow based entropic centrality.

The notion of entropic centrality measures how central a node is in terms of how uncertain the destination of a flow starting at this node is: the more uncertain the destination, the more well connected and thus central the node is deemed. This implicitly assumes that the flow is indivisible, and at every node, the flow is transferred from one edge to another. The contribution of this paper is to propose a split-and-transfer flow model for entropic centrality, where at every node, the flow can actually be arbitrarily split across choices of neighbours. We show how to map this to an equivalent transfer entropic centrality set-up for the ease of computation, and carry out three case studies (an airport network, a cross-shareholding network and a Bitcoin transactions subnetwork) to illustrate the interpretation and insights linked to this new notion of centrality.