Network Analysis on Incomplete Structures.

Over the past decade, networks have become an increasingly popular abstraction for problems in the physical, life, social and information sciences. Network analysis can be used to extract insights into an underlying system from the structure of its network representation. One of the challenges of applying network analysis is the fact that networks do not always have an observed and complete structure. This dissertation focuses on the problem of imputation and/or inference in the presence of incomplete network structures. I propose four novel systems, each of which, contain a module that involves the inference or imputation of an incomplete network that is necessary to complete the end task. I first propose EdgeBoost, a meta-algorithm and framework that repeatedly applies a non-deterministic link predictor to improve the efficacy of community detection algorithms on networks with missing edges. On average EdgeBoost improves performance of existing algorithms by 7% on artificial data and 17% on ego networks collected from Facebook. The second system, Butterworth, identifies a social network user's topic(s) of interests and automatically generates a set of social feed ``rankers'' that enable the user to see topic specific sub-feeds. Butterworth uses link prediction to infer the missing semantics between members of a user's social network in order to detect topical clusters embedded in the network structure. For automatically generated topic lists, Butterworth achieves an average top-10 precision of 78%, as compared to a time-ordered baseline of 45%. Next, I propose Dobby, a system for constructing a knowledge graph of user-defined keyword tags. Leveraging a sparse set of labeled edges, Dobby trains a supervised learning algorithm to infer the hypernym relationships between keyword tags. Dobby was evaluated by constructing a knowledge graph of LinkedIn's skills dataset, achieving an average precision of 85% on a set of human labeled hypernym edges between skills. Lastly, I propose Lobbyback, a system that automatically identifies clusters of documents that exhibit text reuse and generates ``prototypes'' that represent a canonical version of text shared between the documents. Lobbyback infers a network structure in a corpus of documents and uses community detection in order to extract the document clusters.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133443/1/mattburg_1.pd

Defining Collective Identities in Technopolitical Interaction Networks

We are currently witnessing the emergence of new forms of collective identities and a redefinition of the old ones through networked digital interactions, and these can be explicitly measured and analyzed. We distinguish between three major trends on the development of the concept of identity in the social realm: (1) an essentialist sense (based on conditions and properties shared by members of a group), (2) a representational or ideational sense (based on the application of categories by oneself or others), and (3) a relational and interactional sense (based on interaction processes between actors and their environments). The interactional approach aligns with current empirical and methodological progress in social network analysis. Moreover, it has been argued that, within the network society, the notion of collective identity (Melucci, 1995) in the political field must be rethought as technologically mediated and interactive. We suggest that collective identities should be understood asrecurrent,cohesive, andcoordinated communicative interaction networks.We here propose that such identities can be depicted by: (a) mapping and filtering a relevant interaction network, (b) delimiting a set of communities, (c) determining the strongly connected component(s) of such communities (the core identity) in a directed graph, and (d) defining the identity audiences and sources within the community. This technical graph-theoretical characterization is explained and justified in detail through a toy model and applied to three empirical case studies to characterize political identities in party politics (communicative interaction in Twitter during the Spanish elections in 2018), contentious politics in confrontation (in Twitter during the Catalan strike for independence 2019), and the multitudinous identity of Spanish Indignados/15 social movement (in Facebook fan pages 2011). We discuss how the proposed definition is useful to delimit and characterize the internal structure of collective identities in technopolitical interaction networks, and we suggest how the proposed methods can be improved and complemented with other approaches. We finally draw the theoretical implications of understanding collective identities as emerging from interaction networks in a progressive platformization of social interactions in a digital world.XB and AC-L acknowledge the funding from projects "Inter-identidad" FFI2014-52173-P by the MINECO, Spanish Government, and from the Spanish Ministry of Science and Innovation with project Outonomy PID2019-104576GB-I00. XB also acknowledges IAS-Research Group funding IT-1228-19 from the Basque Government. EC acknowledges the funding from the project "Foment de la recerca participativa i de la innovacio digital li democratica a traves de laboratoris ciutadans" by the Barcelona City Council

Understanding the Popularity Evolution of Online Media: A Case Study on YouTube Videos

Understanding the popularity evolution of online media has become an important research topic. There are a number of key questions which have high scientific significance and wide practical relevance. In particular, what are the statistical characteristics of online user behaviors? What are the main factors that affect online collective attention? How can one predict the popularity of online content? Recently, researchers have tried to understand the way popularity evolves from both a theoretical and empirical perspective. A number of important insights have been gained: e.g., most videos obtain the majority of their viewcounts at the early stage after uploading; for videos having identical content, there is a strong “first-mover” advantage, so that early uploads have the most views; YouTube video viewcount dynamics strongly correlate with video quality. Building upon these insights, the main contributions of the thesis are: we proposed two new representations of viewcount dynamics. One is popularity scale where we represent each video’s popularity by their relative viewcount ranks in a large scale dataset. The other is the popularity phase which models the rise and fall of video’s daily viewcount overtime; We also proposed four computational tools. The first is an efficient viewcount phase detection algorithm which not only automatically determines the number of phases each video has, but also finds the phase parameters and boundaries. The second is a phase-aware viewcount prediction method which utilizes phase information to significantly improve the existing state-of-the-art method. The third is a phase-aware viewcount clustering method which can better capture “pulse patterns” in viewcount data. The fourth is a novel method of predicting viewcounts using external information from the Twitter network. Finally, this thesis sets out results from large-scale, longitudinal measurement study of YouTube video viewcount history, e.g. we find videos with different popularity and categories have distinctive phase histories. And we also observed a non-trivial number of concave phases. Dynamics like this can not be explained in terms of existing models, and the terminology and tools introduced here have the potential to spark fresh analysis efforts and further research. In all, the methods and insights developed in the thesis improve our understanding of online collective attention. They also have considerable potential usage in online marketing, recommendation and information dissemination e.g., in emergency & natural disasters

Topology Reconstruction of Dynamical Networks via Constrained Lyapunov Equations

The network structure (or topology) of a dynamical network is often unavailable or uncertain. Hence, we consider the problem of network reconstruction. Network reconstruction aims at inferring the topology of a dynamical network using measurements obtained from the network. In this technical note we define the notion of solvability of the network reconstruction problem. Subsequently, we provide necessary and sufficient conditions under which the network reconstruction problem is solvable. Finally, using constrained Lyapunov equations, we establish novel network reconstruction algorithms, applicable to general dynamical networks. We also provide specialized algorithms for specific network dynamics, such as the well-known consensus and adjacency dynamics.Comment: 8 page

2019 Oklahoma Research Day Full Program

Oklahoma Research Day 2019 - SWOSU Celebrating 20 years of Undergraduate Research Successes