Exploring Communities in Large Profiled Graphs

Given a graph $G$ and a vertex $q\in G$, the community search (CS) problem aims to efficiently find a subgraph of $G$ whose vertices are closely related to $q$. Communities are prevalent in social and biological networks, and can be used in product advertisement and social event recommendation. In this paper, we study profiled community search (PCS), where CS is performed on a profiled graph. This is a graph in which each vertex has labels arranged in a hierarchical manner. Extensive experiments show that PCS can identify communities with themes that are common to their vertices, and is more effective than existing CS approaches. As a naive solution for PCS is highly expensive, we have also developed a tree index, which facilitate efficient and online solutions for PCS

SEMANTIC DISCOVERY THROUGH TEXT PROCESSING

As the world embraces the digital era, unprecedented volumes of information are generated and consumed daily. It becomes difficult to comb through mountains of documents to locate search topics. With inherent ambiguity in human languages, conventional methods using straight text pattern match cannot resolve words having multiple meanings and often misinterpret user intent. There is a need to develop a system able to identify the target topic and return quality relevant links, ending the tedium of rummaging through piles of unrelated links that may get lost in the rubble. An example search of the words “sound investment” helps to illustrate this point. Both Google and Bing return result sets that disorderly interleave musical services and financial planning links, two very different subject matters. User is left to cherry pick manually among the results for the intended links. To combat this problem, this project seeks to develop a new automated methodology for classifying web content by semantics, featuring machine learning capability that can adapt to a rapidly changing environment. This will enable a new type of search engine that organizes results according to related topics

Explicit and spontaneous breaking of SU(3) into its finite subgroups

We investigate the breaking of SU(3) into its subgroups from the viewpoints of explicit and spontaneous breaking. A one-to-one link between these two approaches is given by the complex spherical harmonics, which form a complete set of SU(3)-representation functions. An invariant of degrees p and q in complex conjugate variables corresponds to a singlet, or vacuum expectation value, in a (p,q)-representation of SU(3). We review the formalism of the Molien function, which contains information on primary and secondary invariants. Generalizations of the Molien function to the tensor generating functions are discussed. The latter allows all branching rules to be deduced. We have computed all primary and secondary invariants for all proper finite subgroups of order smaller than 512, for the entire series of groups \Delta(3n^2), \Delta(6n^2), and for all crystallographic groups. Examples of sufficient conditions for breaking into a subgroup are worked out for the entire T_{n[a]}-, \Delta(3n^2)-, \Delta(6n^2)-series and for all crystallographic groups \Sigma(X). The corresponding invariants provide an alternative definition of these groups. A Mathematica package, SUtree, is provided which allows the extraction of the invariants, Molien and generating functions, syzygies, VEVs, branching rules, character tables, matrix (p,q)_{SU(3)}-representations, Kronecker products, etc. for the groups discussed above.Comment: 62 pages, 5 figures; the corresponding software package SUtree can be downloaded from http://theophys.kth.se/~amerle/SUtree/SUtree.html New in v2: Nice figure added, references added, explicit transformation matrices between different embeddings calculated, software package update

Depression, anxiety, and burnout in academia: topic modeling of PubMed abstracts

The problem of mental health in academia is increasingly discussed in literature, and to extract meaningful insights from the growing amount of scientific publications, text mining approaches are used. In this study, BERTopic, an advanced method of topic modeling, was applied to abstracts of 2,846 PubMed articles on depression, anxiety, and burnout in academia published in years 1975–2023. BERTopic is a modular technique comprising a text embedding method, a dimensionality reduction procedure, a clustering algorithm, and a weighing scheme for topic representation. A model was selected based on the proportion of outliers, the topic interpretability considerations, topic coherence and topic diversity metrics, and the inevitable subjectivity of the criteria was discussed. The selected model with 27 topics was explored and visualized. The topics evolved differently with time: research papers on students' pandemic-related anxiety and medical residents' burnout peaked in recent years, while publications on psychometric research or internet-related problems are yet to be presented more amply. The study demonstrates the use of BERTopic for analyzing literature on mental health in academia and sheds light on areas in the field to be addressed by further research