Network Analysts Research Articles

Analysis and Geovisualization of Tsunami Hazard and Evacuation Routes in the Opak-Progo Coastal Area

The southern coastal area of Java has a high risk of tsunamis. Additionally, the presence of rivers flowing in the south of Java Island poses a greater tsunami threat because these rivers can act as "toll roads" for tsunami waves to enter the land. Therefore, this study was conducted in the coastal area of the Opak-Progo watershed to determine the level of tsunami hazards and plan effective evacuation routes. The hazard map was created using the Berryman method, which employs parameters such as slope, surface roughness coefficient, coastline, and tsunami run-up height scenario; a 3-metre scenario was used in this study. Network analysts also determined evacuation routes using the nearest facility method. Network analysis was used to identify an optimised route with four evacuation sites. This research has the potential to significantly contribute to tsunami mitigation and evacuation planning in the coastal areas of the Opak-Progo watershed.

Geospatial approach to accessibility of referral hospitals using geometric network analysts and spatial distribution models of covid-19 spread cases based on gis in bekasi city, west java

Bekasi City has a high population density, as seen from its growth rate in 2020. Therefore, geospatial analysis is required to support and provide effective and efficient health services, evaluate the need for referral hospital capacity, and minimize the spread of COVID-19 cases in this city. The geospatial methods used in this study are Geometric Network Analyst and Geographic Weighted Regression (GWR), with Service Area (SA) used for analysis. The results based on the distance between the referral hospitals and settlements in Bekasi City showed that more than 2.201 million people, or 90%, have been well covered. Meanwhile, regarding travel time, 1.792 million people or 73% in eight sub-districts are in well-served areas. Conversely, referral hospitals do not cover four sub-districts, namely Bantar Gebang, Jati Sampurna, Medan Satria, and Jati Asih. The spatial modeling analysis results using GWR with spatial-temporal data recapitulation of data reports for eight months showed predictions for the spread of confirmed cases in six sub-districts, namely West Bekasi, North Bekasi, East Bekasi, Medan Satria, Mustika Jaya, and Rawalumbu. This implies that local governments need to suggest more referral hospitals serving people who live far from the existing referral hospitals.

Advertising as an example of media persuasiveness with the use of modern types of narration

The purpose of this article is to present the main persuasive function of various types of narratives based on the analysis of messages designed to influence and impel to make a decision. An additional purpose is to show that different narrative forms were used thoughtfully based on the research into intended recipient groups. The used methodology of narrative analysis in relation to media messages, the function of which is persuasion, was created on the basis of the methodology of content analysis in mediametric studies of the discipline of social communication and media, as well as related sciences, for example, visual, linguistic or semiological analysis. The relevance of the study is confirmed by the use of fresh material taken from current ratings conducted by network analysts regarding the most popular advertising messages in the Internet space and modern tools in the field of neuroscience. This made it possible to single out such types of narratives as cathectic, storytelling, celebrity, gaming and amplified narratives. Among the analyzed messages, including commercials and social campaigns, the authors highlight their distinctive features and ways of using them in the field of language and images, which makes it possible to categorize any multimodal persuasive messages, and facilitates the interpretation of this type of messages. Demonstrating the persuasiveness of the narrative as an integral feature of all messages in advertising communication leads to the conclusion that the purpose of using the narrative is to create tension and dynamics that strongly affect the viewer. Ultimately, the reason for its use is not only a reference, but also a description of the world. The narrative also has significant axiological and philosophical goals.

Visualizing Uncertainty in Probabilistic Graphs with Network Hypothetical Outcome Plots (NetHOPs).

Probabilistic graphs are challenging to visualize using the traditional node-link diagram. Encoding edge probability using visual variables like width or fuzziness makes it difficult for users of static network visualizations to estimate network statistics like densities, isolates, path lengths, or clustering under uncertainty. We introduce Network Hypothetical Outcome Plots (NetHOPs), a visualization technique that animates a sequence of network realizations sampled from a network distribution defined by probabilistic edges. NetHOPs employ an aggregation and anchoring algorithm used in dynamic and longitudinal graph drawing to parameterize layout stability for uncertainty estimation. We present a community matching algorithm to enable visualizing the uncertainty of cluster membership and community occurrence. We describe the results of a study in which 51 network experts used NetHOPs to complete a set of common visual analysis tasks and reported how they perceived network structures and properties subject to uncertainty. Participants' estimates fell, on average, within 11% of the ground truth statistics, suggesting NetHOPs can be a reasonable approach for enabling network analysts to reason about multiple properties under uncertainty. Participants appeared to articulate the distribution of network statistics slightly more accurately when they could manipulate the layout anchoring and the animation speed. Based on these findings, we synthesize design recommendations for developing and using animated visualizations for probabilistic networks.

Including animals in sociology.

How do we include animals in sociology? Although sociology's initial avoidance of the nonhuman world may have been necessary to the field's development, recent scholarship - within mainstream sociology, environmental sociology and animal-centred research - is helping expand the field's horizons. With a focus on variety, this article reviews four key paths that researchers are taking to include animals in their research: (1) studying interspecies relations, (2) theorizing animals as an oppressed group, (3) investigating the social and ecological impacts of animal agriculture and (4) analysing social-ecological networks. This review shows how applying - and innovating - existing social theories and research methods allows researchers to include animals in their analyses and will be relevant to a variety of scholars, including mainstream and environmental sociologists, animal-focused researchers and social network analysts, to name a few.

A concluding comment: Toward a critical social network analysis

The authors of articles in this special issue on “Ethics in SNA” contribute to enriching the continuing discussion of major ethical dilemmas that social network analysts need to confront. These articles encourage us to replace silence with thoughtful consideration and innovative paths forward. I highlight three themes that emerged in my own reading of these contributions: moving beyond “ethics versus science,” deepening reflexivity, and possible approaches toward a critical SNA.

Data mining approach to analyzing intrusion detection of wireless sensor network

<span>Wireless sensor network (WSN) is a collection of wireless sensor nodes which are distributed in nature and a base station where the dispersed nodes are used to monitor and the physical conditions of the environment is recorded and then these data are organized into the base. Its application has been reached out from critical military application such as battlefield surveillance to traffic, health, industrial areas, intruder detection, security and surveillance. Due to various features in WSN it is very prone to various types external attacks. Preventing such attacks, intrusion detection system (IDS) is very important so that attacker cannot steal or manipulate data. Data mining is a technique that can help to discover patterns in large dataset. This paper proposed a data mining technique for different types of classification algorithms to detect denial of service (DoS) attacks which is of four types. They are Grayhole, Blackhole, Flooding and TDMA. A number of data mining techniques, such as KNN, Naïve Bayes, Logistic Regression, support vector machine (SVM) and ANN algorithms are applied on the dataset and analyze their performance in detecting the attacks. The analysis reveals the applicability of these algorithms for detecting and predicting such attacks and can be recommended for network specialist and analysts. </span>

The Problem of Shaping the Capacity of Sovereign Powers of a State in the Digital Age

The global architecture of computer networks poses a security dilemma: while modern information and communication technologies allow adversaries to challenge the exclusive power of states over “his own” cyberspace, following traditional notions of sovereignty, respectively, may limit the ability of states to actively counter challenges and threats in global networks. The aim of the study is to determine the possible scope of application of the traditional territorial concept of sovereignty to the activities of states in cyberspace. The article shows that regarding the activities of states and individuals in cyberspace, the scope of the principle of sovereignty is determined by the practical imperatives of states and depends on the sphere of interstate and cross-border interactions affected. Therefore, for example, for the purposes of warfare, there are three levels at which objects for destruction will be determined: physical network, logical network, network user. Other criteria are important for developers, operators and network analysts, and, according to a number of proposals, a global network can include up to seven levels (physical, data transfer, network, transport, session, data, applications). The article demonstrates that despite the exceptional nature of the powers and jurisdictions of the state in relation to the physical level of cyberspace, its logical and social levels are open to cross-border manifestations of the jurisdiction of other states on the basis of the proximity criterion. That is, for those cases when states can establish a real connection with digital objects or online personalities, and, accordingly, exercise authority.

To rely or not to rely on common sense? Introducing critical Realism's insights to social network analysis

AbstractIn this article, I aim to critically evaluate the epistemological position of social network analysis. My main point is that while the two main schools of social network analysis – formalism and relationalism – claim to be different because of their respective comprehensions of the ontology of social networks, epistemologically they both rely on network actors' commonsensical comprehension of social ties to conduct social network analysis. Following critical realists' insights into the depth ontology of the social world, I argue that social network analysts must break from their and network actors' commonsensical comprehension of social ties in order to illustrate the objective principles of social networks. I suggest that one strategy for doing so is to treat the current networks of social actors under investigation as a product of historically shaped and state‐mediated contests among social actors to define hierarchical features of their social relations. I present my study of the history of social networks in Indian politics during the early postcolonial period (1947–64) in support of my points.

Chile as a Transpacific Bridge: Brokerage and Social Capital in the Pacific Basin

ABSTRACT The idea of Chile as a ‘bridge country’ that connects – or should connect – Latin America with the Asia-Pacific region has guided its commercial diplomacy since the early 1990s. We argue that this geopolitical vision corresponds closely with what network analysts describe as a ‘broker’: an actor that bridges ‘structural holes’ by connecting otherwise disconnected actors or groups of actors. As a relational approach, social network analysis provides an alternative way of thinking about Chile’s position in international relations that does not rely on preconceived groupings or rank orders based on country attributes, as in the case of ‘middle powers’ and ‘small states.’ Further, the approach allows us to empirically explore which countries of the Pacific Basin currently perform such a role and whether (and when) Chile conforms to the theoretical expectations of a ‘transpacific broker.’ Using network data on commercial agreements (1980–2018), we trace Chile’s emergence as a transpacific broker and discuss how its commercial diplomacy provided the country with leverage in this regard. In doing so, the study sheds light on the opportunities and limitations of peripheral actors seeking to gain social capital through networks of relations.

Do Managers’ Concurrent-Post Ties Affect Analysts’ Forecast Accuracy?

This paper examines whether the network ties help explain variation in analysts’ forecast accuracy and how the relationship between network ties and analysts’ forecast accuracy varies depending on the nature and context of those ties. We posit that the analysts could develop a valuable relationship with the chairman or CEO through their covering process, and thus acquire information of those managers’ concurrent-post companies through this concurrent-post ties. We find that the analysts who cover both companies provided formal job for chairman or CEO (called “focus firms”) and companies provided concurrent-post job for those managers (called “peer firms”) during the same year issue more accurate forecasts for the peer firms. This phenomenon is more significant especially when the chairman or CEO of the focus firms has worked more than two years in the peer firms, the peer firms’ earnings quality is low, institutional investors hold more shareholdings in the peer firms, analysts offer more efforts for the focus firms, and the managers of the focus firms hold the managers’ posts in the peer firms. Further research finds that the higher the proportion of focus-peer firm pairs in analyst portfolio, the less accuracy of the other firms covered by the analysts who follow both the focus firms and the peer firms simultaneously, suggesting that those analysts need invest more efforts to maintain a relationship with the focus firms’ managers. Finally, this paper also finds that analysts tend to issue optimistic forecasts for the focus firms during the litigation, in order to reward managers who offer private information of peer firms to analysts though their social network formed in the experience of concurrent posts.

Treemaps as a Tool for Social Network Analysis

Abstract : We apply treemap technology in the analysis of large, social network datasets principally for examining network subgroups. A treemap is graphically-based information and exploration tool which is used in diverse fields such as computer science, finance, and human-gene research; from our experience, we find treemaps also useful in the social network analysis setting. A treemap represents hierarchical and categorical data in a mosaic form containing embedded, rectangular shapes, where the size of each shape is germane. Treemap displays are especially helpful when examining data in an interactive mode (as opposed to a static or printed form). We have found that treemaps are a powerful tool for exploring large social-networks, particularly during the exploratory data analysis phase. Their use quickly leads to a thorough perspective of the holistic characteristics of the network and to easier identification of significant subgroups; both of these perspectives may otherwise remain hidden using traditional visualization techniques. In this report, we introduce treemap technology, first broadly, then, specifically how it can be applied to social network analysis. We also show how we have actually applied treemaps to an interactive study of a large, real-world dataset. As a result of our experiences, we encourage other social network analysts to consider applying treemaps in their work.

LENTA: Longitudinal Exploration for Network Traffic Analysis From Passive Data

In this paper, we present longitudinal exploration for network traffic analysis (LENTA), a system that supports the network analysts in the identification of traffic generated by services and applications running on the Web. In the case of URLs observed in operative network, LENTA simplifies the analyst’s job by letting her observe few hundreds of clusters instead of the original hundred thousands of single URLs. We implement a self-learning methodology, where the system grows its knowledge, which is used in turn to automatically associate traffic to previously observed services, and identify new traffic generated by possibly suspicious applications. This approach lets the analysts easily observe changes in network traffic, identify new services, and unexpected activities. We follow a data-driven approach and run LENTA on traces collected both in ISP networks and directly on hosts via proxies. We analyze traffic in batches of 24-h worth of traffic. Big data solutions are used to enable horizontal scalability and meet performance requirements. We show that LENTA allows the analyst to clearly understand which services are running on their network, possibly highlighting malicious traffic and changes over time, greatly simplifying the view and understanding of the network traffic.

Clustering and evolutionary approach for longitudinal web traffic analysis

In recent years, data-driven approaches have attracted the interest of the research community. Considering network monitoring, unsupervised machine learning solutions such as clustering are particularly appealing to let the network analysts observe patterns, and track the evolution of traffic over time. In this paper, we present a novel unsupervised methodology to automatically process and analyze batches of HTTP traffic, looking just at the URL structure. First, we describe IDBSCAN, Iterative-DBSCAN. We design it to obtain well-shaped clusters, and to simplify the choice of parameters — often a cumbersome step for the network analyst. Second, we show LENTA, Longitudinal Exploration for Network Traffic Analysis, which allows to automatically observe the evolution over time of traffic, naturally highlighting trends and pinpointing anomalies.We first evaluate IDBSCAN and LENTA on synthetic data to compare their performance against well-known algorithms. Then we apply them on a real case, facing the analysis of hundred thousands of URLs collected from a live network. Results show both the goodness of clusters produced by IDBSCAN and LENTA ability to highlight changes in traffic, facilitating the analyst job.

Impact of class distribution on the detection of slow HTTP DoS attacks using Big Data

The integrity of modern network communications is constantly being challenged by more sophisticated intrusion techniques. Attackers are consistently shifting to stealthier and more complex forms of attacks in an attempt to bypass known mitigation strategies. In recent years, attackers have begun to focus their attack efforts on the application layer, allowing them to produce attacks that can exploit known issues within specific application protocols. Slow HTTP Denial of Service attacks are one such attack variant, which targets the HTTP protocol and can imitate legitimate user traffic in order to deny resources from a service. Successful mitigation of this attack type requires network analysts to evaluate large quantities of network traffic to identify and block intrusive traffic. The issue, is that the number of legitimate traffic instances can far outnumber the amount of attack instances, making detection problematic. Machine learning techniques can be used to aid in detection, but the large level of imbalance between normal (majority) and attack (minority) instances can lead to inaccurate detection results. In this work, we evaluate the use of data sampling to produce varying class distributions in order to counteract the effects of severely imbalanced Slow HTTP DoS big datasets. We also detail our process for collecting real-world representative Slow HTTP DoS attack traffic from a live network environment to create our datasets. Five class distributions are generated to evaluate the Slow HTTP DoS detection performance of eight machine learning techniques. Our results show that the optimal learner and class distribution combination is that of Random Forest with a 65:35 distribution ratio, obtaining an AUC value of 0.99904. Further, we determine through the use of significance testing, that the use of sampling techniques can significantly increase learner performance when detecting Slow HTTP DoS attack traffic.

Framework for risk assessment in cyber situational awareness

A large number of data is generated to help network analysts to evaluate the network security situation in traditional detection and prevention measures, but it is not used fully and effectively, there is not a holistic view of the network situation on it for now. To address this issue, a framework is proposed to evaluate the security situation of the network from three dimensions: threat, vulnerability and stability, and merge the results at decision level to measure the security situation of the overall network. In the case studies, the authors demonstrate how the framework is deployed in the network and how to use it to reflect the security situation of the network in real time. Results of the case study show that the framework can evaluate the security situation of the network accurately and reasonably.

Enhancing Network Visibility and Security through Tensor Analysis

The increasing size, variety, rate of growth and change, and complexity of network data has warranted advanced network analysis and services. Tools that provide automated analysis through traditional or advanced signature-based systems or machine learning classifiers suffer from practical difficulties. These tools fail to provide comprehensive and contextual insights into the network when put to practical use in operational cyber security. In this paper, we present an effective tool for network security and traffic analysis that uses high-performance data analytics based on a class of unsupervised learning algorithms called tensor decompositions. The tool aims to provide a scalable analysis of the network traffic data and also reduce the cognitive load of network analysts and be network-expert-friendly by presenting clear and actionable insights into the network.In this paper, we demonstrate the successful use of the tool in two completely diverse operational cyber security environments, namely, (1) security operations center (SOC) for the SCinet network at the SuperComputing (SC) Conference in 2016 and 2017 and (2) Reservoir Labs’ Local Area Network (LAN). In each of these environments, we produce actionable results for cyber security specialists including (but not limited to) (1) finding malicious network traffic involving internal and external attackers using port scans, SSH brute forcing, and NTP amplification attacks, (2) uncovering obfuscated network threats such as data exfiltration using DNS port and using ICMP traffic, and (3) finding network misconfiguration and performance degradation patterns.

How Should We Study Relational Structure? Critically Comparing the Epistemological Positions of Social Network Analysis and Field Theory

I argue that the main difference between two schools of relational sociology – field theory and social network analysis – lies in the difference between their respective epistemological stances rather than between their ontological assumptions. While social network analysts have developed sophisticated quantitative, qualitative and mixed methods, they epistemologically rely on their commonsensical understanding of relational structure. In contrast, field theorists are expected to study relational structure by making an epistemological break from their commonsensical understanding of relational structure. Social network analysts’ epistemological position reveals only social ties as the form of relational structure. Field theory’s epistemological position reveals multiple forms of relational structure, including but not limited to those formed by social ties. The main lesson to be learned is that relational sociologists must develop their notion of relational structure by investigating the history of contests among field actors over the meaning of being a member of their field.

GUIRO: User-Guided Matrix Reordering.

Matrix representations are one of the main established and empirically proven to be effective visualization techniques for relational (or network) data. However, matrices-similar to node-link diagrams-are most effective if their layout reveals the underlying data topology. Given the many developed algorithms, a practical problem arises: "Which matrix reordering algorithm should I choose for my dataset at hand?" To make matters worse, different reordering algorithms applied to the same dataset may let significantly different visual matrix patterns emerge. This leads to the question of trustworthiness and explainability of these fully automated, often heuristic, black-box processes. We present GUIRO, a Visual Analytics system that helps novices, network analysts, and algorithm designers to open the black-box. Users can investigate the usefulness and expressiveness of 70 accessible matrix reordering algorithms. For network analysts, we introduce a novel model space representation and two interaction techniques for a user-guided reordering of rows or columns, and especially groups thereof (submatrix reordering). These novel techniques contribute to the understanding of the global and local dataset topology. We support algorithm designers by giving them access to 16 reordering quality metrics and visual exploration means for comparing reordering implementations on a row/column permutation level. We evaluated GUIRO in a guided explorative user study with 12 subjects, a case study demonstrating its usefulness in a real-world scenario, and through an expert study gathering feedback on our design decisions. We found that our proposed methods help even inexperienced users to understand matrix patterns and allow a user-guided steering of reordering algorithms. GUIRO helps to increase the transparency of matrix reordering algorithms, thus helping a broad range of users to get a better insight into the complex reordering process, in turn supporting data and reordering algorithm insights.

In Social Network Analysis, Which Centrality Index Should I Use? Theoretical Differences and Empirical Similarities among Top Centralities

This research examines four frequently used centrality indices — degree, closeness, betweenness, and eigenvectors — to understand the extent to which their clear theoretical distinctions are reflected in differences in empirical performance. Even for stylized networks in which one centrality index may seem more relevant than the others, the four indices are frequently highly correlated. This result can be interpreted as good news: it does not diminish the conceptual distinctions, yet it suggests the indices are rather robust, yielding similar information about actors’ positions in networks, which can be reassuring given their widespread use by applied network analysts who may not appreciate the theoretically distinct origins and definitions. This research also compares computational speed across the centrality indices as another practical element that may help determine the choice of centrality index.