A visual multivariate dynamic egocentric network exploration tool

Visualizing multivariate dynamic networks is a challenging task. The evolution of the dynamic network within the temporal axis must be depicted in conjunction with the associated multivariate attributes. In this thesis, an exploratory visual analytics tool is proposed to display multivariate dynamic networks with spatial attributes. The proposed tool displays the distribution of multivariate temporal domain and network attributes in scattered views. Moreover, in order to expose the evolution of a single or a group of nodes in the dynamic network along the temporal axis, an egocentric approach is applied in which a node is represented with its neighborhood as an ego-network. This approach allows users to observe a node's surrounding environment along the temporal axis. On top of the traditional ego-network visualization methods, such as timelines, the proposed tool encodes ego-networks as feature vectors consisting of the domain and network attributes and projects them onto 2D views. As a result, distances between projected ego-networks represent the dissimilarity across temporal axis in a single view. The proposed tool is demonstrated with a real-world use case scenario on merchant networks obtained from a one-year long credit card transaction

One City, Two Tales: Using Mobility Networks to Understand Neighborhood Resilience and Fragility during the COVID-19 Pandemic

What predicts a neighborhood's resilience and adaptability to essential public health policies and shelter-in-place regulations that prevent the harmful spread of COVID-19? To answer this question, in this paper we present a novel application of human mobility patterns and human behavior in a network setting. We analyze mobility data in New York City over two years, from January 2019 to December 2020, and create weekly mobility networks between Census Block Groups by aggregating Point of Interest level visit patterns. Our results suggest that both the socioeconomic and geographic attributes of neighborhoods significantly predict neighborhood adaptability to the shelter-in-place policies active at that time. That is, our findings and simulation results reveal that in addition to factors such as race, education, and income, geographical attributes such as access to amenities in a neighborhood that satisfy community needs were equally important factors for predicting neighborhood adaptability and the spread of COVID-19. The results of our study provide insights that can enhance urban planning strategies that contribute to pandemic alleviation efforts, which in turn may help urban areas become more resilient to exogenous shocks such as the COVID-19 pandemic

Facilitating decision-making with multimodal interfaces in collaborative analytical sessions

In collaborative visual analytics sessions, participants analyze data and cooperate toward a shared vision. These decision-making processes are challenging and time-consuming. In this chapter, we introduce a system for facilitating decision-making in exploratory and collaborative visual analytics sessions. Our system comprises an assistant analytical agent, a multi-display wall and a framework for interactive visual analytics. The assistant agent understands participants’ ongoing conversations and exhibits information about the data on displays. The displays are also used to manifest the current state of the session. In addition, the agent answers the participants’ questions either regarding the data or open-domain ones, and preserves the productivity and the efficiency of the session by confirming that the participants do not deviate from the session’s goal. Whereas, our visual analytics medium makes data tangible, hence more comprehensible and natural to operate with. The results of our qualitative study indicate that the proposed system fosters productive multi-user decision-making processes

Keystroke dynamics based biometric identification [Tuş vuruşa dayalı biyometrik tanıma]

Biometrics based keystroke dynamics aim to perform user identification and authentication based on users' typing behaviour on digital devices. In this study, keystroke timing and regional distributions extracted from free-text are utilized to perform user identification. In order to obtain the highest representative set of attributes, attributes based on directional graph, hold time and keyboard distance have been extracted and used in different configurations. In order to process the generated feature sets more effectively, unlike the existing studies, a multilayer artificial neural network model with attention mechanism was used and 0.13% FAR and 2.5% FRR results were obtained

Visual analytic based ship collision probability modeling for ship navigation safety

This study presents a tangible visual analytic tool to analyse maritime traffic in spatio-temporal basis using AIS data. This novel approach helps in understanding the macroscopic safety structure of both fairways and individual ships with evidences in microscopic level. Qualification of our system is demonstrated with 7-days AIS trajectory collected from Mexican Gulf. We find out that spatio-temporal position pattern of encountered ships in Port Houston varies over time, significantly. In addition, the spatial distribution of ship accidents coincide with proposed near-miss density areas. Furthermore, proposed tool is capable of capturing real accident cases. Field experiments with domain experts have demonstrated that our approach helps in making realistic inferences about navigational safety behaviour of both individual vessel and water area

Footfall prediction using graph neural networks [Çizge sinir ağları ile yaya trafiği tahmini]

Accurately predicting the potential foot traffic for a new business is a crucial task since it directly impacts a business's ability to generate revenue. In this work, a graph neural networkbased approach is introduced in which the foot traffic between businesses and neighborhoods is represented in a bipartite network setting where edges capture the yearly-aggregated foot traffic quartile labels. Resulting bipartite networks are fed to the graph neural network to predict the foot traffic label for a new business for all the available neighborhoods. The graph neural network model outperforms well-established Huff model by 3% higher F1 score. Our results indicate that utilizing graph neural network architectures for foot traffic prediction is promising and requires more attention from the field

Finding proper time intervals for dynamic network extraction

Extracting a proper dynamic network for modeling a time-dependent complex system is an important issue. Building a correct model is related to finding out critical time points where a system exhibits considerable change. In this work, we propose to measure network similarity to detect proper time intervals. We develop three similarity metrics, node, link, and neighborhood similarities, for any consecutive snapshots of a dynamic network. Rather than a label or a user-defined threshold, we use statistically expected values of proposed similarities under a null-model to state whether the system changes critically. We experimented on two different data sets with different temporal dynamics: the Wi-Fi access points logs of a university campus and Enron emails. Results show that, first, proposed similarities reflect similar signal trends with network topological properties with less noisy signals, and their scores are scale invariant. Second, proposed similarities generate better signals than adjacency correlation with optimal noise and diversity. Third, using statistically expected values allows us to find different time intervals for a system, leading to the extraction of non-redundant snapshots for dynamic network modeling

An exploratory visual analytics tool for multivariate dynamic networks

Visualizing multivariate dynamic networks is a challenging task. The evolution of the dynamic network within the temporal axis must be depicted in conjunction with the associated multivariate attributes. In this paper, an exploratory visual analytics tool is proposed to display multivariate dynamic networks with spatial attributes. The proposed tool displays the distribution of multivariate temporal domain and network attributes in scattered views. Moreover, in order to expose the evolution of a single or a group of nodes in the dynamic network along the temporal axis, an egocentric approach is applied in which a node is represented with its neighborhood as an ego-network. This approach allows users to observe a node's surrounding environment along the temporal axis. On top of the traditional ego-network visualization methods, such as timelines, the proposed tool encodes ego-networks as feature vectors consisting of the domain and network attributes and projects them onto 2D views. As a result, the distance between projected ego-networks represents the dissimilarity across the temporal axis in a single view. The proposed tool is demonstrated with a real-world use case scenario on merchant networks obtained from a one-year-long credit card transactions

Predicting merchant future performance using privacy-safe network-based features

Abstract Small and Medium-sized Enterprises play a significant role in most economies by contributing to job creation and economic growth. A majority of such merchants rely on business financing, and thus, financial institutions and investors need to assess their performance before making decisions on business loans. However, current methods of predicting merchants’ future performance involve their private internal information, such as revenue and customer base, which cannot be shared without potentially exposing critical information. To address this problem, we first propose a novel approach to predicting merchants’ future performance using credit card transaction data. Specifically, we construct a merchant network, regarding customers as bridges between merchants, and extract features from the constructed network structure for prediction purposes. Our study results demonstrate that the performance of machine learning models with features extracted from our proposed network is comparable to those with conventional revenue- and customer-based features, while maintaining higher privacy levels when shared with third-party organizations. Our approach offers a practical solution to privacy concerns over data and information required for merchants’ performance prediction, enabling safe data-sharing among financial institutions and investors, helping them make more informed decisions on allocating their financial resources while ensuring that merchants’ sensitive information is kept confidential