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      The shocklet transform: A decomposition method for the identification of local, mechanism-driven dynamics in sociotechnical time series

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          Abstract

          We introduce an unsupervised pattern recognition algorithm termed the Discrete Shocklet Transform (DST) by which local dynamics of time series can be extracted. Time series that are hypothesized to be generated by underlying deterministic mechanisms have significantly different DSTs than do purely random null models. We apply the DST to a sociotechnical data source, usage frequencies for a subset of words on Twitter over a decade, and demonstrate the ability of the DST to filter high-dimensional data and automate the extraction of anomalous behavior.

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          Singularity detection and processing with wavelets

          Naji Mallat, W.L. Hwang (1992)
          Article 0 comments Cited 289 times – based on 0 reviews     Review now
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            Finding community structure in very large networks.

            Aaron Clauset, M. Newman, Cristopher Moore (2004)
            The discovery and analysis of community structure in networks is a topic of considerable recent interest within the physics community, but most methods proposed so far are unsuitable for very large networks because of their computational cost. Here we present a hierarchical agglomeration algorithm for detecting community structure which is faster than many competing algorithms: its running time on a network with n vertices and m edges is O (md log n) where d is the depth of the dendrogram describing the community structure. Many real-world networks are sparse and hierarchical, with m approximately n and d approximately log n, in which case our algorithm runs in essentially linear time, O (n log(2) n). As an example of the application of this algorithm we use it to analyze a network of items for sale on the web site of a large on-line retailer, items in the network being linked if they are frequently purchased by the same buyer. The network has more than 400 000 vertices and 2 x 10(6) edges. We show that our algorithm can extract meaningful communities from this network, revealing large-scale patterns present in the purchasing habits of customers.
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              Temporal patterns of happiness and information in a global social network: Hedonometrics and Twitter

              , , … (2011)
              Individual happiness is a fundamental societal metric. Normally measured through self-report, happiness has often been indirectly characterized and overshadowed by more readily quantifiable economic indicators such as gross domestic product. Here, we examine expressions made on the online, global microblog and social networking service Twitter, uncovering and explaining temporal variations in happiness and information levels over timescales ranging from hours to years. Our data set comprises over 46 billion words contained in nearly 4.6 billion expressions posted over a 33 month span by over 63 million unique users. In measuring happiness, we use a real-time, remote-sensing, non-invasive, text-based approach---a kind of hedonometer. In building our metric, made available with this paper, we conducted a survey to obtain happiness evaluations of over 10,000 individual words, representing a tenfold size improvement over similar existing word sets. Rather than being ad hoc, our word list is chosen solely by frequency of usage and we show how a highly robust metric can be constructed and defended.
              Article 0 comments Cited 163 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Publication date Created: 27 June 2019
                Article
                ArXiV ID: 1906.11710
                SO-VID: 36b54c80-2693-4c3f-b1d2-180d6b2a4f3b
                License:

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Custom metadata
                Comments 12 pages, 11 figures
                Categories physics.soc-ph physics.data-an

                ScienceOpen disciplines: General physics,Mathematical & Computational physics
                Data availability:
                ScienceOpen disciplines: General physics, Mathematical & Computational physics

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