Understanding Teamwork Using Dynamic Network Models

Studying team processes is critical to understanding how teams work to achieve team outcomes. To effectively study team processes, behavioral activities team members enact must be measured with sufficient granularity and intensity. Analyzing the detailed mechanics of team processes requires employing analytical methods sensitive to modeling the series of actions and interactions of team members as they execute taskwork and teamwork over time. Current empirical investigation of team processes lags with respect to intricately measuring and assessing team processes over time. Using dynamic network models, this dissertation sought to understand the behaviors responsible for interaction patterns amongst team members, how those interaction patterns and structures relate to team member behavior, and how interactive team processes relate to team outcomes. Specifically, this dissertation utilized interaction-level data from the National Basketball Association (NBA) and applied three dynamic network models to the data: Separable Temporal Exponential Random Graph Modeling (STERGM), Stochastic Actor-Oriented Modeling (SAOM), and Relational Event Modeling (REM). The purpose of this dissertation is to provide a descriptive foundation for future studies using theories of time to study team phenomena and to demonstrate the utility of dynamic network models. This dissertation details the theoretical foundations of team processes and network analysis, the temporal extensions of traditional network analyses, the utility and applicability of dynamic network models (STERGM, SAOM and REM) using NBA data, and shows insights these methods provide for studying team processes. Results of this dissertation showed reciprocity to be the strongest passing pattern amongst NBA teams, followed by transitive passing patterns. Specifically, NBA players in the 2016-2017 season frequently formed mutual (between two players) and transitive (between three players) passing relations. Player position and scoring behavior were not found to influence passing patterns, nor was home versus away status. Forming mutual and transitive ties related to team wins based on STERGM analyses but similar passing patterns were not found to predict wins with REM analyses, reinforcing methodological and analytical differences in these dynamic network methods. This dissertation discusses the applicability, utility, and implications of applying these dynamic network models to studying team processes and provides practical information about how these methods can be used to inform future research and practice on team dynamics

Aspects of Estimation Procedures at Eurostat with Some Emphasis on Over-Space Harmonisation

It is of high interest for Eurostat, the investigation of the different estimation procedures that are applied, or discussed, internally. We focus our interest on three estimation domains i.e. the micro-aggregation techniques for producing confidential data, the backward calculation methods for obtaining homogeneous time series and some aspects of the sampling procedures that are discussed by Eurostat and are applied in the Member State level. With regard to each domain of estimation, we describe the different estimation procedures that are applied and the criteria for assessing the quality of the results obtained, and we make some proposals for the adoption of better practices. Due to the multinational character of the third estimation domain and in order to achieve the targets of our description, we used as exploratory tools three sample surveys that are conducted in all Member State i.e. the Labour Force survey, the European Household Panel survey and the Household Budget survey. Especially for those estimation domains that are applied at National level, we examined attempts that aim at the over space harmonization of the estimation procedures or of the measured concepts, and the role that Eurostat adopts in relation to those harmonization attempts

Representation learning on relational data

Humans utilize information about relationships or interactions between objects for orientation in various situations. For example, we trust our friend circle recommendations, become friends with the people we already have shared friends with, or adapt opinions as a result of interactions with other people. In many Machine Learning applications, we also know about relationships, which bear essential information for the use-case. Recommendations in social media, scene understanding in computer vision, or traffic prediction are few examples where relationships play a crucial role in the application. In this thesis, we introduce methods taking relationships into account and demonstrate their benefits for various problems. A large number of problems, where relationship information plays a central role, can be approached by modeling data by a graph structure and by task formulation as a prediction problem on the graph. In the first part of the thesis, we tackle the problem of node classification from various directions. We start with unsupervised learning approaches, which differ by assumptions they make about the relationship's meaning in the graph. For some applications such as social networks, it is a feasible assumption that densely connected nodes are similar. On the other hand, if we want to predict passenger traffic for the airport based on its flight connections, similar nodes are not necessarily positioned close to each other in the graph and more likely have comparable neighborhood patterns. Furthermore, we introduce novel methods for classification and regression in a semi-supervised setting, where labels of interest are known for a fraction of nodes. We use the known prediction targets and information about how nodes connect to learn the relationships' meaning and their effect on the final prediction. In the second part of the thesis, we deal with the problem of graph matching. Our first use-case is the alignment of different geographical maps, where the focus lies on the real-life setting. We introduce a robust method that can learn to ignore the noise in the data. Next, our focus moves to the field of Entity Alignment in different Knowledge Graphs. We analyze the process of manual data annotation and propose a setting and algorithms to accelerate this labor-intensive process. Furthermore, we point to the several shortcomings in the empirical evaluations, make several suggestions on how to improve it, and extensively analyze existing approaches for the task. The next part of the thesis is dedicated to the research direction dealing with automatic extraction and search of arguments, known as Argument Mining. We propose a novel approach for identifying arguments and demonstrate how it can make use of relational information. We apply our method to identify arguments in peer-reviews for scientific publications and show that arguments are essential for the decision process. Furthermore, we address the problem of argument search and introduce a novel approach that retrieves relevant and original arguments for the user's queries. Finally, we propose an approach for subspace clustering, which can deal with large datasets and assign new objects to the found clusters. Our method learns the relationships between objects and performs the clustering on the resulting graph

A network model of interpersonal alignment in dialog

In dyadic communication, both interlocutors adapt to each other linguistically, that is, they align interpersonally. In this article, we develop a framework for modeling interpersonal alignment in terms of the structural similarity of the interlocutors’ dialog lexica. This is done by means of so-called two-layer time-aligned network series, that is, a time-adjusted graph model. The graph model is partitioned into two layers, so that the interlocutors’ lexica are captured as subgraphs of an encompassing dialog graph. Each constituent network of the series is updated utterance-wise. Thus, both the inherent bipartition of dyadic conversations and their gradual development are modeled. The notion of alignment is then operationalized within a quantitative model of structure formation based on the mutual information of the subgraphs that represent the interlocutor’s dialog lexica. By adapting and further developing several models of complex network theory, we show that dialog lexica evolve as a novel class of graphs that have not been considered before in the area of complex (linguistic) networks. Additionally, we show that our framework allows for classifying dialogs according to their alignment status. To the best of our knowledge, this is the first approach to measuring alignment in communication that explores the similarities of graph-like cognitive representations. Keywords: alignment in communication; structural coupling; linguistic networks; graph distance measures; mutual information of graphs; quantitative network analysi

Web knowledge bases

Knowledge is key to natural language understanding. References to specific people, places and things in text are crucial to resolving ambiguity and extracting meaning. Knowledge Bases (KBs) codify this information for automated systems — enabling applications such as entity-based search and question answering. This thesis explores the idea that sites on the web may act as a KB, even if that is not their primary intent. Dedicated kbs like Wikipedia are a rich source of entity information, but are built and maintained at an ongoing cost in human effort. As a result, they are generally limited in terms of the breadth and depth of knowledge they index about entities. Web knowledge bases offer a distributed solution to the problem of aggregating entity knowledge. Social networks aggregate content about people, news sites describe events with tags for organizations and locations, and a diverse assortment of web directories aggregate statistics and summaries for long-tail entities notable within niche movie, musical and sporting domains. We aim to develop the potential of these resources for both web-centric entity Information Extraction (IE) and structured KB population. We first investigate the problem of Named Entity Linking (NEL), where systems must resolve ambiguous mentions of entities in text to their corresponding node in a structured KB. We demonstrate that entity disambiguation models derived from inbound web links to Wikipedia are able to complement and in some cases completely replace the role of resources typically derived from the KB. Building on this work, we observe that any page on the web which reliably disambiguates inbound web links may act as an aggregation point for entity knowledge. To uncover these resources, we formalize the task of Web Knowledge Base Discovery (KBD) and develop a system to automatically infer the existence of KB-like endpoints on the web. While extending our framework to multiple KBs increases the breadth of available entity knowledge, we must still consolidate references to the same entity across different web KBs. We investigate this task of Cross-KB Coreference Resolution (KB-Coref) and develop models for efficiently clustering coreferent endpoints across web-scale document collections. Finally, assessing the gap between unstructured web knowledge resources and those of a typical KB, we develop a neural machine translation approach which transforms entity knowledge between unstructured textual mentions and traditional KB structures. The web has great potential as a source of entity knowledge. In this thesis we aim to first discover, distill and finally transform this knowledge into forms which will ultimately be useful in downstream language understanding tasks

Inductive Meta-path Learning for Schema-complex Heterogeneous Information Networks

Heterogeneous Information Networks (HINs) are information networks with multiple types of nodes and edges. The concept of meta-path, i.e., a sequence of entity types and relation types connecting two entities, is proposed to provide the meta-level explainable semantics for various HIN tasks. Traditionally, meta-paths are primarily used for schema-simple HINs, e.g., bibliographic networks with only a few entity types, where meta-paths are often enumerated with domain knowledge. However, the adoption of meta-paths for schema-complex HINs, such as knowledge bases (KBs) with hundreds of entity and relation types, has been limited due to the computational complexity associated with meta-path enumeration. Additionally, effectively assessing meta-paths requires enumerating relevant path instances, which adds further complexity to the meta-path learning process. To address these challenges, we propose SchemaWalk, an inductive meta-path learning framework for schema-complex HINs. We represent meta-paths with schema-level representations to support the learning of the scores of meta-paths for varying relations, mitigating the need of exhaustive path instance enumeration for each relation. Further, we design a reinforcement-learning based path-finding agent, which directly navigates the network schema (i.e., schema graph) to learn policies for establishing meta-paths with high coverage and confidence for multiple relations. Extensive experiments on real data sets demonstrate the effectiveness of our proposed paradigm