Change point detection in social networks—Critical review with experiments

Change point detection in social networks is an important element in developing the understanding of dynamic systems. This complex and growing area of research has no clear guidelines on what methods to use or in which circumstances. This paper critically discusses several possible network metrics to be used for a change point detection problem and conducts an experimental, comparative analysis using the Enron and MIT networks. Bayesian change point detection analysis is conducted on different global graph metrics (Size, Density, Average Clustering Coefficient, Average Shortest Path) as well as metrics derived from the Hierarchical and Block models (Entropy, Edge Probability, No. of Communities, Hierarchy Level Membership). The results produced the posterior probability of a change point at weekly time intervals that were analysed against ground truth change points using precision and recall measures. Results suggest that computationally heavy generative models offer only slightly better results compared to some of the global graph metrics. The simplest metrics used in the experiments, i.e. nodes and links numbers, are the recommended choice for detecting overall structural changes.     

Peer-exchange schemes to handle mismatch in peer-to-peer systems

A self-organizing peer-to-peer system is built upon an application level overlay, whose topology is independent of an underlying physical network. A well-routed message path in such systems may result in a long delay and excessive traffic due to the mismatch between logical and physical networks. In order to solve this problem, we present a family of Peer-exchange Routing Optimization Protocols (PROP) to reconstruct the overlay. It includes two policies: PROP-G for generic condition and PROP-O for optimized one. Both theoretical analysis and simulation experiments show that these two protocols greatly reduce the average latency of the overlay and achieve a better logical topology with low overhead. Their overall performance can be further improved if combined with other recent approaches. Specifically, PROP-G can be easily applied to both structured and unstructured systems without the loss of their primary characteristics, such as efficient routing and anonymity. PROP-O, on the other hand, is more efficient, especially in a heterogenous environment where nodes have different processing capabilities.

Detecting communities and their evolutions in dynamic social networks—a Bayesian approach

Although a large body of work is devoted to finding communities in static social networks, only a few studies examined the dynamics of communities in evolving social networks. In this paper, we propose a dynamic stochastic block model for finding communities and their evolution in a dynamic social network. The proposed model captures the evolution of communities by explicitly modeling the transition of community memberships for individual nodes in the network. Unlike many existing approaches for modeling social networks that estimate parameters by their most likely values (i.e., point estimation), in this study, we employ a Bayesian treatment for parameter estimation that computes the posterior distributions for all the unknown parameters. This Bayesian treatment allows us to capture the uncertainty in parameter values and therefore is more robust to data noise than point estimation. In addition, an efficient algorithm is developed for Bayesian inference to handle large sparse social networks. Extensive experimental studies based on both synthetic data and real-life data demonstrate that our model achieves higher accuracy and reveals more insights in the data than several state-of-the-art algorithms.     

Modelling uncertainty in social–natural interactions

Socio-ecological systems can be represented as a complex network of causal interactions. Modelling such systems requires methodologies that are able to take uncertainty into account. Due to their probabilistic nature, Bayesian networks are a powerful tool for representing complex systems where interactions between variables are subject to uncertainty. In this paper, we study the interactions between social and natural subsystems (land use and water flow components) using hybrid Bayesian networks based on the Mixture of Truncated Exponentials model. This study aims to provide a new methodology to model systemic change in a socio-ecological context. Two endogenous changes – agricultural intensification and the maintenance of traditional cropland – are proposed. Intensification of the agricultural practices leads to a rise in the rate of immigration to the area, as well as to greater water losses through evaporation. By contrast, maintenance of traditional cropland hardly changes the social structure, while increasing evapotranspiration rates and improving the control over runoff water. These results indicate that hybrid Bayesian networks are an excellent tool for modelling social–natural interactions.     

Designing energy efficient target tracking protocol with quality monitoring in wireless sensor networks

Target tracking is one application of wireless sensor networks and energy efficient target tracking algorithms that can be used for accurate tracking are highly desired. In order to achieve energy savings, we focus on reducing energy usage by limiting the number of sensors used to track a target through monitoring their data quality and by limiting the amount of data being sent to the cluster head. We propose an energy efficient target tracking protocol that uses two algorithms to accomplish this goal. Simulation studies show that the network lifetime is extended up to 35% with application of both algorithms and that the side effect on target tracking accuracy is not too negative.     

What distinguish one from its peers in social networks?

Being able to discover the uniqueness of an individual is a meaningful task in social network analysis. This paper proposes two novel problems in social network analysis: how to identify the uniqueness of a given query vertex, and how to identify a group of vertices that can mutually identify each other. We further propose intuitive yet effective methods to identify the uniqueness identification sets and the mutual identification groups of different properties. We further conduct an extensive experiment on both real and synthetic datasets to demonstrate the effectiveness of our model.     

Cultural differences,experience with social networks and the nature of “true commitment” in Facebook

Formed on an analysis of design practices, the behaviour chain model stipulates that social network designer’s ultimate aim is to encourage users to adopt the social network site by entering a phase of true commitment. During this phase, social network users are driven to connect to known or unknown others by engaging in instrumental uses that create value and content and involve others, while staying active and loyal by investing time in the site. This paper investigates how designer’s intentions, as captured by the behaviour chain model, materialise through users’ reported practices in the social network site Facebook. A total of 423 Facebook users from 5 countries answered a questionnaire that allowed us to examine how 2 user characteristics, experience with the site, and culture, shape the nature of true commitment. Our findings show that experience with the site and even more so, culture, have an effect on users’ motivations for using Facebook, as well as their instrumental uses and the time they invest on the site. This analysis reifies the behaviour chain model by allowing designers to understand how the features they design are embodied in users’ practices.     

Analytical modeling of interconnection networks in heterogeneous multi-cluster systems

The study of interconnection networks is important because the overall performance of a distributed system is often critically hinged on the effectiveness of its interconnection network. This paper addresses the problem of interconnection networks performance modeling of large-scale distributed systems with emphases on heterogeneous multi-cluster computing systems. We present an analytical model to predict message latency in multi-cluster systems in the presence of node, network and system organization heterogeneity. The model is validated through comprehensive simulation, which demonstrates that the proposed model exhibits a good degree of accuracy for various system organizations and under different working conditions.

Distributed multi-hop cooperative communication in dense wireless sensor networks

In this paper, we investigate the use of cooperative communications for high performance data dissemination in dense wireless sensor networks. We first identify the limitations of existing cooperative schemes. While we previously proposed a multi-hop cooperative data dissemination scheme, REER, to address these limitations, the construction of such structure relies on a pre-established reference path. The partially centralized approach makes REER unscalable when encountering network dynamics. To address this issue, this paper proposes a novel distributed multi-hop cooperative communication scheme (DMC), which is fully distributed and consists of two operation phases: (1) cooperative mesh structure (CMS) construction, and (2) CMS-based data dissemination, which includes random value-based scheme and distance-based scheme for forwarding node selection. Simulation results show that DMC performs well in terms of a number of QoS metrics, and fits well in large-scale networks and highly dynamic environments.     

Analytical modeling and comparison of fault-tolerant message flow control mechanisms in torus-connected networks

In many environments, rather than minimizing message latency or maximizing network performance, the ability to survive beyond the failure of individual network components is the main issue of interests. The nature of Wormhole Switching (WS) leads to high network throughput and low message latencies. However, in the vicinity of faulty regions, these behaviors cause rapid congestion, provoking the network becomes deadlocked. While techniques such as adaptive routing can alleviate the problem, they cannot completely solve the problem. Thus, there have been extreme studies on other types of switching mechanisms in networking and multicomputers communities. In this paper, we present a general mathematical model to assess the relative performance merits of three well-known fault-tolerant switching methods in tori, namely Scouting Switching (SS), Pipelined Circuit Switching (PCS), and Circuit Switching (CS). We have carried out extensive simulation experiments, the results of which are used to validate the proposed analytical models. We have also conducted an extensive comparative performance analysis, by means of analytical modeling, of SS, PCS, and CS under various working conditions. The analytical results reveal that SS shows substantial performance improvements for low to moderate failure rates over PCS and CS, which achieves close to WS performance. PCS can provide superior performance over CS and behaves the same or in some occasions worse than SS, under light and moderate traffic, especially with the same hardware requirements.     

Dynamics of opinion formation,social power evolution,and naïve learning in social networks

The past few decades have witnessed a prevalence of applying dynamical models to the study of social networks. This paper reviews recent advances in the investigation of social networks with a predominant focus on agent-based models. Starting from classical models of opinion dynamics, we survey several recently developed models on opinion formation and social power evolution. These models extend the classical models’ cognitive assumption that individuals’ opinions evolve on a single issue by incorporating various sociological or psychological hypotheses to account for the evolution of opinions over multiple or a sequence of interdependent issues. We summarize basic results on the asymptotic behaviors of these models and discuss their sociological interpretations. In addition, we show how these models play a role in the emergence of collective intelligence by applying them to a naïve learning setting. Novel results that reveal how individuals successfully learn an unknown truth over issue sequences are presented. Finally, we conclude the paper and discuss potential directions for future research.     

Exploiting slack time for just-in-time scheduling in wireless sensor networks

We consider the problem of real-time data collection in wireless sensor networks, in which data need to be delivered to one or more sinks within end-to-end deadlines. To enhance performance with respect to end-to-end deadline miss ratio, existing approaches schedule packets by prioritizing them based on per-packet deadlines and other factors such as the distance to the sink. However, important factors affecting the end-to-end performance such as queuing delays and buffer overruns have largely been ignored in the existing real-time schemes. Packet prioritization by itself cannot assist with these issues, and may in fact, exacerbate them for real-time data collection, since many high priority packets may simultaneously contend for the constrained network resources. In sensor networks, where the channel bandwidth and buffer space are often quite limited, these issues can dramatically impact real-time performance. Based on this observation, we propose Just-in-Time Scheduling (JiTS) strategies where packets are judiciously delayed within their slack time to reduce contention and load balance the use of the network buffers. We explore several policies for delaying data packets at different intermediate nodes considering potential contention. In addition, we also show that the routing protocol has a significant impact on real-time performance. In particular, shortest path routing leads to considerably better performance than geographic forwarding, which is often used for real-time data transmission in wireless sensor networks. Using an extensive simulation study, we demonstrate that JiTS can significantly improve the deadline miss ratio and packet drop ratio compared to two state-of-the-art approaches for real-time packet delivery for sensor networks (RAP and SPEED) under various scenarios. Notably, JiTS requires neither lower layer (e.g., MAC layer) support nor synchronization among the sensor nodes.     

Managing workload in human–robot interaction: A review of empirical studies

Working with artificial agents is a challenging endeavor, often imposing high levels of workload on human operators who work within these socio-technical systems. We seek to understand these workload demands through examining the literature in major content areas of human–robot interaction. As research on HRI continues to explore a host of issues with operator workload, there is a need to synthesize the extant literature to determine its current state and to guide future research. Within HRI socio-technical systems, we reviewed the empirical literature on operator information processing and action execution. Using multiple resource theory (MRT; Wickens, 2002) as a guiding framework, we organized this review by the operator perceptual and responding demands which are routinely manipulated in HRI studies. We also reviewed the utility of different interventions for reducing the strain on the perceptual system (e.g., multimodal displays) and responses (e.g., automation). Our synthesis of the literature demonstrates that much is known about how to decrease operator workload, but there are specific gaps in knowledge due to study operations and methodology. This work furthers our understanding of workload in complex environments such as those found when working with robots. Principles and propositions are provided for those interested in decreasing operator workload in applied settings and also for future research.     

Team formation in social networks based on collective intelligence – an evolutionary approach

The tremendous growth of the social web has inspired research communities to discover social intelligence, which encompasses a wide spectrum of knowledge characterized by human interaction, communication and collaboration, thereby exploiting collective intelligence (CI) to support the successful existence of social communities on the Web. In this work, we address the team formation problem for generalized tasks where a set of experts is to be discovered from an expertise social network that can collaborate effectively to accomplish a given task. The concept of CI that emerges from these collaborations attempts to maximize the potential of the team of experts, rather than only aggregating individual potentials. Because the team formation problem is NP-hard, a genetic algorithm-based approach is applied to optimize computational collective intelligence in web-based social networks. To capture the essence of CI, a novel quantitative measure Collective Intelligence Index (CII) is proposed that takes two factors into account –the “enhanced expertise score” and the “trust-based collaboration score”. This measure relates to the social interactions among experts, reflecting various affiliations that form a network of experts that help to drive creativity by deepening engagements through collaboration and the exchange of ideas and expertise, thereby enriching and enhancing the knowledge base of experts. The presented model also captures the teams’ dynamics by considering trust, which is essential to effective interactions between the experts. The computational experiments are performed on a synthetic dataset that bears close resemblance to real-world expertise networks, and the results clearly establish the effectiveness of our proposed model.     

Quantifying engagement: Measuring player involvement in human–avatar interactions

This research investigated the merits of using an established system for rating behavioral cues of involvement in human dyadic interactions (i.e., face-to-face conversation) to measure involvement in human–avatar interactions. Gameplay audio–video and self-report data from a Feasibility Trial and Free Choice study of an effective peer resistance skill building simulation game (DRAMA–RAMA™) were used to evaluate reliability and validity of the rating system when applied to human–avatar interactions. The Free Choice study used a revised game prototype that was altered to be more engaging. Both studies involved girls enrolled in a public middle school in Central Florida that served a predominately Hispanic (greater than 80%), low-income student population. Audio–video data were coded by two raters, trained in the rating system. Self-report data were generated using measures of perceived realism, predictability and flow administered immediately after game play. Hypotheses for reliability and validity were supported: reliability values mirrored those found in the human dyadic interaction literature. Validity was supported by factor analysis, significantly higher levels of involvement in Free Choice as compared to Feasibility Trial players, and correlations between involvement dimension sub scores and self-report measures. Results have implications for the science of both skill-training intervention research and game design.     

Communication-resource-aware adaptive watermarking for multimedia authentication in wireless multimedia sensor networks

Efficient multimedia and specifically image authentication is critical and in demand to protect data vulnerability in wireless multimedia sensor networks (WMSN). This is to prevent malicious intruders from modifying and forging image contents over a network. Watermarking technique has been widely used to assert an image data authentication over wired networks; however, resource constraints (e.g. processing power, communication energy) in small sensors and the state of error-prone wireless channels result in fundamental challenges for developing efficient watermarking schemes in WMSN. These challenges include how to embed/protect/extract watermark efficiently and robustly in low-cost sensors and how to transmit authenticated image and multimedia with high energy efficiency. In this paper, we propose a communication-resource-aware and adaptive watermarking scheme for multimedia authentication in WMSN. Our contribution is two folds. First, the transmission quality for the watermark as well as watermarked multimedia authenticity by embedding watermark with adaptive coding redundancies, and by unequally allocating network resources to protect the image and multimedia packets with the watermark information. Second, communication energy efficiency and real time performance are achieved with the watermark being adaptive to the network condition and the processing delay reduced due to the exploited inter-frame correlation. The simulation and experimental results demonstrate that the proposed adaptive watermarking system can achieve considerable gains in terms of energy saving, image transmission quality and multimedia authentication performance.     

Distributed scheduling algorithms for channel access in TDMA wireless mesh networks

In this paper, we have considered the distributed scheduling problem for channel access in TDMA wireless mesh networks. The problem is to assign time-slot(s) for nodes to access the channels, and it is guaranteed that nodes can communicate with all their one-hop neighbors in the assigned time-slot(s). And, the objective is to minimize the cycle length, i.e., the total number of different time-slots in one scheduling cycle. In single-channel ad hoc networks, the best known result for this problem is proved to be K ² in arbitrary graphs (Chlamtac and Pinter in IEEE Trans. Comput. C-36(6):729–737, 1987) and 25K in unit disk graphs () with K as the maximum node degree. There are multiple channels in wireless mesh networks, and different nodes can use different control channels to reduce congestion on the control channels. In this paper, we have considered two scheduling models for wireless mesh networks. The first model is that each node has two radios, and the scheduling is simultaneously done on the two radios. We have proved that the upper bound of the cycle length in arbitrary graphs can be 2K. The second model is that the time-slots are scheduled for the nodes regardless of the number of radios on them. In this case, we have proved that the upper bound can be (4K−2). We also have proposed greedy algorithms with different criterion. The basic idea of these algorithms is to organize the conflicting nodes by special criterion, such as node identification, node degree, the number of conflicting neighbors, etc. And, a node cannot be assigned to a time-slot(s) until all neighbor nodes, which have higher criterion and might conflict with the current node, are assigned time-slot(s) already. All these algorithms are fully distributed and easy to realize. Simulations are also done to verify the performance of these algorithms.     

Dynamic admission control in hybrid QoS networks with WiFi access

Recent developments in the IEEE 802.11 standardizations[1] have been successful to offer high-speed data services. Hence, traffic classes (e.g. VoIP or video-conference) with different QoS requirements will be provided in future wireless LANs (WLAN). Sinc…     

Flexible multibody approach in forward dynamic simulation of locomotive strains in human skeleton with flexible lower body bones

A method for bone strain estimation is examined in this article. The flexibility of a single bone in an otherwise rigid human skeleton model has been studied previously by various authors. However, in the previous studies, the effect of the flexibility of multiple bones on the musculoskeletal model behavior was ignored. This study describes a simulation method that can be used to estimate the bone strains at both tibias and femurs of a 65-year-old Caucasian male subject. The verification of the method is performed by the comparison of the results with other studies available in literature. The results of the study show good correlation with the results of previous empirical studies. A damping effect of the flexible bones on the model is also studied in this paper.