Visualization of Trace Links and Change Impact Analysis

Efficient algorithms for game-theoretic betweenness centrality

Group centralization of network indices

This paper is concerned with distributed computation of several commonly used centrality measures in complex networks. In particular, we propose deterministic algorithms, which converge in finite time, for the distributed computation of the degree, closeness and betweenness centrality measures in directed graphs. Regarding eigenvector centrality, we consider the PageRank problem as its typical variant, and design distributed randomized algorithms to compute PageRank for both fixed and time-varying graphs. A key feature of the proposed algorithms is that they do not require to know the network size, which can be simultaneously estimated at every node, and that they are clock-free. To address the PageRank problem of time-varying graphs, we introduce the novel concept of persistent graph, which eliminates the effect of spamming nodes. Moreover, we prove that these algorithms converge almost surely and in the sense of $L^p$. Finally, the effectiveness of the proposed algorithms is illustrated via extensive simulations using a classical benchmark.

Decision making is a critical challenge under disruptive conditions. Information flow as critical component to decision making may exacerbate or mitigate the impact of disruptive conditions. Such highly complex and unpredictable conditions produce a high demand for information. Extant literature was found to be critical of unidirectional and sender-oriented models in crisis communication which essentially blocks the feedback loop and paralyzes the crisis managers to roll-out responses. However, the pervasive social media has created new patterns of dynamic information exchange to empower the individuals to be engaged in a more evidence-based participatory form of crisis communication to frame decisions. This study used social media analytics to identify the decision clusters arising out of information exchange over social media under disruptive uncertainties. The Theory of Complexity by Edgar Morin was used as the epistemological foundation of the study. Crawled data was used from the Tourism Tribe Facebook fan page. Normalized degree and betweenness centrality measures were used to analyse the semantic networks. Tourism Tribe exhibited the highest degree centralities for both before and during the pandemic-driven disruptions. Four individuals (names changed for anonymity) were identified to be the most influential in decision making based on information shared. The causality between uncertainty and decision revealed a wide array of decision parameters – health precautions to alternative leisure engagement. The study provided practical implications for a number of stakeholders. The tourism service providers and product designers are likely to get deeper insights into the choice and priorities of the tourists confronting disruptive conditions and may re-strategise tourism offers. The alternative leisure and recreational platforms (OTT, gaming platforms etc.) may introduce new products and/or re-design their schemes.

오피니언 마이닝과 네트워크 분석을 활용한 상품 커뮤니티 분석: 영화 흥행성과 예측 사례

In the discussion concerning network interactions above I gave several example of how to use network theory to intervene against the spread of infectious diseases. In general a policy maker would like to lengthen the characteristic path length in the network. In doing so she may intervene by vaccinating individuals with high degree or high betweenness centrality measures. In doing so it makes the structure of the interaction network less able to efficiently spread an infectious disease. There have been many studies that have looked at these types of strategies in real world populations. For instance, I, along with Philip Polgreen and Alberto Segre, look at how allocating a limited amount of vaccine by giving it first to the individuals with highest degree in the network, as opposed to randomly, greatly limits the spread of infectious disease in a hospital. Others use social interaction data to investigate how different vaccination programs change the expected size of epidemics.

In network analysis, the betweenness centrality of a node informally captures\nthe fraction of shortest paths visiting that node. The computation of the\nbetweenness centrality measure is a fundamental task in the analysis of modern\nnetworks, enabling the identification of the most central nodes in such\nnetworks. Additionally to being massive, modern networks also contain\ninformation about the time at which their events occur. Such networks are often\ncalled temporal networks. The temporal information makes the study of the\nbetweenness centrality in temporal networks (i.e., temporal betweenness\ncentrality) much more challenging than in static networks (i.e., networks\nwithout temporal information). Moreover, the exact computation of the temporal\nbetweenness centrality is often impractical on even moderately-sized networks,\ngiven its extremely high computational cost. A natural approach to reduce such\ncomputational cost is to obtain high-quality estimates of the exact values of\nthe temporal betweenness centrality. In this work we present ONBRA, the first\nsampling-based approximation algorithm for estimating the temporal betweenness\ncentrality values of the nodes in a temporal network, providing rigorous\nprobabilistic guarantees on the quality of its output. ONBRA is able to compute\nthe estimates of the temporal betweenness centrality values under two different\noptimality criteria for the shortest paths of the temporal network. In\naddition, ONBRA outputs high-quality estimates with sharp theoretical\nguarantees leveraging on the \\emph{empirical Bernstein bound}, an advanced\nconcentration inequality. Finally, our experimental evaluation shows that ONBRA\nsignificantly reduces the computational resources required by the exact\ncomputation of the temporal betweenness centrality on several real world\nnetworks, while reporting high-quality estimates with rigorous guarantees.\n

Epidemics of certain viruses in a population can have major impact effects, as is the case in the recent global pandemic caused by the COVID-19 virus. Identifying infected individuals during the course of an epidemic is extremely important for measuring spread and designing more effective control measures. However, in some epidemics infected individuals do not exhibit clear symptoms despite being infected and contributing to the contagion of others (called asymptomatic). This work addresses the problem of identifying asymptomatic individuals in network epidemics based on the observation of infected (symptomatic) individuals. The main contribution of this work is the evaluation of different centrality measures to identify asymptomatic individuals when a fraction of the infected nodes in a network epidemic is observed at a given moment in time. In particular, a variation of the betweenness centrality measure is proposed in this work. An evaluation using different network models and different asymptomatic rates shows that the proposed centrality measure outperforms other centrality measures in many scenarios. Furthermore, the performance of centrality measures increases as the fraction of asymptomatic decreases, showing an interesting trade-off.

Due to serious demand-supply imbalances, many airports around the world are highly congested. Access to these highly congested (Level 3, coordinated) airports is controlled through the use of the IATA World Airport Slot allocation Guidelines (WASG). At an individual airport, slots requested by each airline are allocated at the airport under consideration independently without taking into account the interactions between slots allocated at different airports. However, in order for the air-transport network to operate seamlessly, ensuring network-wide connectivity of flights, and the interdependencies existing between the slots allocated at individual airports need to be considered. Several models have been proposed in the literature to deal with the optimum allocation of slots at a single airport. However, the literature currently does not adequately address the network-wide slot allocation problem. In this paper, we are introducing a novel approach to address the network-wide slot allocation problem. Our approach considers as an input the individual airport schedules generated during the slot allocation process at individual airports and optimally adjusts them to ensure network-wide flight connectivity by taking into account the interdependencies existing between flights connecting pairs of airports.To this end, we propose bi-objective mathematical models, which consider schedule efficiency and inter-airline fairness objectives, and incorporate the importance that different airports have for the functioning of the air transport network, using the IATA connectivity indices and the betweenness centrality measures. We solve the proposed models using the ε − constraint method to investigate trade-offs between network-wide schedule efficiency and fairness, and we investigate the effect of these trade-offs on the airlines and the airports. Results from the application of the proposed models to a test network suggest that the consideration of the contribution of the airports to network connectivity affect the way that the total network-wide schedule displacement is distributed among the airports. Specifically, we found that the use of the IATA connectivity index tends to allocate less schedule displacement to airports with frequent flights to many destinations, while the use of the betweenness centrality measure allocates less schedule displacement to airports that are more critical in ensuring the connectivity of other airports in the network.

BackgroundRapid identification of subject experts for medical topics helps in improving the implementation of discoveries by speeding the time to market drugs and aiding in clinical trial recruitment, etc. Identifying such people who influence opinion through social network analysis is gaining prominence. In this work, we explore how to combine named entity recognition from unstructured news articles with social network analysis to discover opinion leaders for a given medical topic.MethodsWe employed a Conditional Random Field algorithm to extract three categories of entities from health-related new articles: Person, Organization and Location. We used the latter two to disambiguate polysemy and synonymy for the person names, used simple rules to identify the subject experts, and then applied social network analysis techniques to discover the opinion leaders among them based on their media presence. A network was created by linking each pair of subject experts who are mentioned together in an article. The social network analysis metrics (including centrality metrics such as Betweenness, Closeness, Degree and Eigenvector) are used for ranking the subject experts based on their power in information flow.ResultsWe extracted 734,204 person mentions from 147,528 news articles related to obesity from January 1, 2007 through July 22, 2010. Of these, 147,879 mentions have been marked as subject experts. The F-score of extracting person names is 88.5%. More than 80% of the subject experts who rank among top 20 in at least one of the metrics could be considered as opinion leaders in obesity.ConclusionThe analysis of the network of subject experts with media presence revealed that an opinion leader might have fewer mentions in the news articles, but a high network centrality measure and vice-versa. Betweenness, Closeness and Degree centrality measures were shown to supplement frequency counts in the task of finding subject experts. Further, opinion leaders missed in scientific publication network analysis could be retrieved from news articles.

Covert networks are social networks that often consist of harmful users. Social Network Analysis (SNA) has played an important role in reducing criminal activities (e.g., counter terrorism) via detecting the influential users in such networks. There are various popular measures to quantify how influential or central any vertex is in a network. As expected, strategic and influential miscreants in covert networks would try to hide herself and her partners (called \em leaders ) from being detected via these measures by introducing new edges. Waniek et al. show that the corresponding computational problem, called Hiding Leader, is NP Complete for the degree and closeness centrality measures. We study the popular core centrality measure and show that the problem is NP Complete even when the core centrality of every leader is only 3. On the contrary, we prove that the problem becomes polynomial time solvable for the degree centrality measure if the degree of every leader is bounded above by any constant. We then focus on the optimization version of the problem and show that the Hiding Leader problem admits a 2 factor approximation algorithm for the degree centrality measure. We complement it by proving that one cannot hope to have any (2-epsilon) factor approximation algorithm for any constant $epsilon>0$ unless there is a epsilon/2 factor polynomial time algorithm for the Densest k-Subgraph problem which would be considered a significant breakthrough. We empirically establish that our 2 factor approximation algorithm frequently finds out a near optimal solution. On the contrary, for the core centrality measure, we show that the Hiding Leader problem does not admit any (1-alpha) ln n factor approximation algorithm for any constant alpha in (0,1) unless P=NP even when the core centrality of every leader is only 3. Hence, our work shows that, although classical complexity theoretic framework fails to shed any light on relative difficulty of Hiding Leader for different centrality measures, the problem is significantly "harder'' for the core centrality measure than the degree centrality one.

Many studies on coauthorship networks focus on network topology and network statistical mechanics. This article takes a different approach by studying micro‐level network properties with the aim of applying centrality measures to impact analysis. Using coauthorship data from 16 journals in the field of library and information science (LIS) with a time span of 20 years (1988–2007), we construct an evolving coauthorship network and calculate four centrality measures (closeness centrality, betweenness centrality, degree centrality, and PageRank) for authors in this network. We find that the four centrality measures are significantly correlated with citation counts. We also discuss the usability of centrality measures in author ranking and suggest that centrality measures can be useful indicators for impact analysis.

Software change impact analysis (CIA) methods enable developers to understand potential impacts of a code change so that the change can be executed confidently without affecting reliability of the software. However, existing CIA approaches do not support CIA for all source code granularities. Additionally, they lack support for inter-granular change impact queries and hidden dependencies. This paper introduces Augur, an automated static code analysis-based CIA approach that addresses these shortcomings. Augur infers and maintains semantic and environment dependencies along with data and control dependencies between source code entities across granularities. Additionally, Augur uses Change Impact Query Language, a novel query language for impact analysis proposed in this paper, to support inter-granular CIA queries with batch querying feature. Augur has been realized as a Visual Studio extension called Augur-Tool. We have conducted quantitative evaluation on two open-source and two industrial projects to assess the accuracy of the tool. Results from the evaluation indicate that Augur provides CIA with high accuracy (average precision 55% and average recall 85%).

Data classification is a major machine learning paradigm, which has been widely applied to solve a large number of real-world problems. Traditional data classification techniques consider only physical features (e.g., distance, similarity, or distribution) of the input data. For this reason, those are called \textit{low-level} classification. On the other hand, the human (animal) brain performs both low and high orders of learning and it has a facility in identifying patterns according to the semantic meaning of the input data. Data classification that considers not only physical attributes but also the pattern formation is referred to as \textit{high-level} classification. Several high-level classification techniques have been developed, which make use of complex networks to characterize data patterns and have obtained promising results. In this paper, we propose a pure network-based high-level classification technique that uses the betweenness centrality measure. We test this model in nine different real datasets and compare it with other nine traditional and well-known classification models. The results show us a competent classification performance.

The documentation of software architecture relations as a kind of traceability information is considered important to help people understand the consequences or ripple-effects of architecture evolution. Traceability information provides a basis for analysing and evaluating software evolution, and consequently, it can be used for tasks like reuse evaluation and improvement throughout the evolution of software. To date, however, none of the published empirical studies on software architecture traceability have examined the validity of these propositions. In this paper, we hypothesize that impact analysis of changes in software architecture can be more efficient when supported by traceability links. To test this hypothesis, we designed two controlled experiments that were conducted to investigate the influence of traceability links on the quantity and quality of retrieved assets during architecture evolution analysis. The results provide statistical evidence that a focus on architecture traceability significantly reduces the quantity of missing and incorrect assets, and increases the overall quality of architecture impact analysis for evolution.