Results 1  10
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26
Time varying graphs and social network analysis: Temporal indicators and metrics
 Artificial Intelligence and Simulation of Behaviour (AISB
, 2011
"... Abstract. Most instruments formalisms, concepts, and metricsfor social networks analysis fail to capture their dynamics. Typical systems exhibit different scales of dynamics, ranging from the finegrain dynamics of interactions (which recently led researchers to consider temporal versions of dista ..."
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Cited by 14 (6 self)
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Abstract. Most instruments formalisms, concepts, and metricsfor social networks analysis fail to capture their dynamics. Typical systems exhibit different scales of dynamics, ranging from the finegrain dynamics of interactions (which recently led researchers to consider temporal versions of distance, connectivity, and related indicators), to the evolution of network properties over longer periods of time. This paper proposes a general formal approach to study networks’ structural evolution for both atemporal and temporal indicators, based respectively on sequences of static graphs and sequences of timevarying graphs that cover successive timewindows. All the concepts and indicators, some of which are new, are expressed using a timevarying graph formalism recently proposed in [10]. Experimental results of the application of atemporal metrics applied to a portion of the scientific community of arXiv are provided. 1
RoleDynamics: Fast Mining of Large Dynamic Networks
 In LSNAWWW
, 2012
"... To understand the structural dynamics of a largescale social, biological or technological network, it may be useful to discover behavioral roles representing the main connectivity patterns present over time. In this paper, we propose a scalable nonparametric approach to automatically learn the str ..."
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Cited by 11 (3 self)
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To understand the structural dynamics of a largescale social, biological or technological network, it may be useful to discover behavioral roles representing the main connectivity patterns present over time. In this paper, we propose a scalable nonparametric approach to automatically learn the structural dynamics of the network and individual nodes. Roles may represent structural or behavioral patterns such as the center of a star, peripheral nodes, or bridge nodes that connect different communities. Our novel approach learns the appropriate structural “role ” dynamics for any arbitrary network and tracks the changes over time. In particular, we uncover the specific global network dynamics and the local node dynamics of a technological, communication, and social network. We identify interesting node and network patterns such as stationary and nonstationary roles, spikes/steps in rolememberships (perhaps indicating anomalies), increasing/decreasing role trends, among many others. Our results indicate that the nodes in each of these networks have distinct connectivity patterns that are nonstationary and evolve considerably over time. Overall, the experiments demonstrate the effectiveness of our approach for fast mining and tracking of the dynamics in large networks. Furthermore, the dynamic structural representation provides a basis for building more sophisticated models and tools that are fast for exploring large dynamic networks.
The Genetic Algorithm as a General Diffusion Model for Social Networks
"... Diffusion processes taking place in social networks are used to model a number of phenomena, such as the spread of human or computer viruses, and the adoption of products in ‘viral marketing ’ campaigns. It is generally difficult to obtain accurate information about how such spreads actually occur, ..."
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Cited by 11 (0 self)
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Diffusion processes taking place in social networks are used to model a number of phenomena, such as the spread of human or computer viruses, and the adoption of products in ‘viral marketing ’ campaigns. It is generally difficult to obtain accurate information about how such spreads actually occur, so a variety of stochastic diffusion models are used to simulate spreading processes in networks instead. We show that a canonical genetic algorithm with a spatially distributed population, when paired with specific forms of Holland’s synthetic hyperplanedefined objective functions, can simulate a large and rich class of diffusion models for social networks. These include standard diffusion models, such as the independent cascade and competing processes models. In addition, our genetic algorithm diffusion model (GADM) can also model complex phenomena such as information diffusion. We demonstrate an application of the GADM to modeling information flow in a large, dynamic social network derived from email headers.
Modeling Dynamic Behavior in Large Evolving Graphs
"... Given a large timeevolving graph, how can we model and characterize the temporal behaviors of individual nodes (and network states)? How can we model the behavioral transition patterns of nodes? We propose a temporal behavior model that captures the “roles ” of nodes in the graph and how they evolv ..."
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Cited by 10 (1 self)
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Given a large timeevolving graph, how can we model and characterize the temporal behaviors of individual nodes (and network states)? How can we model the behavioral transition patterns of nodes? We propose a temporal behavior model that captures the “roles ” of nodes in the graph and how they evolve over time. The proposed dynamic behavioral mixedmembership model (DBMM) is scalable, fully automatic (no userdefined parameters), nonparametric/datadriven (no specific functional form or parameterization), interpretable (identifies explainable patterns), and flexible (applicable to dynamic and streaming networks). Moreover, the interpretable behavioral roles are generalizable and computationally efficient. We applied our model for (a) identifying patterns and trends of nodes and network states based on the temporal behavior, (b) predicting future structural changes, and (c) detecting unusual temporal behavior transitions. The experiments demonstrate the scalability, flexibility, and effectiveness of our model for identifying interesting patterns, detecting unusual structural transitions, and predicting the future structural changes of the network and individual nodes.
Understanding Actor Loyalty to EventBased Groups in Affiliation Networks ∗
, 2010
"... In this paper, we introduce a method for analyzing the temporal dynamics of affiliation networks. We define affiliation groups which describe temporally related subsets of actors and describe an approach for exploring changing memberships in these affiliation groups over time. To model the dynamic b ..."
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Cited by 6 (3 self)
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In this paper, we introduce a method for analyzing the temporal dynamics of affiliation networks. We define affiliation groups which describe temporally related subsets of actors and describe an approach for exploring changing memberships in these affiliation groups over time. To model the dynamic behavior in these networks, we consider the concept of loyalty and introduce a measure that captures an actor’s loyalty to an affiliation group as the degree of ‘commitment ’ an actor shows to the group over time. We evaluate our measure using three real world affiliation networks: a publication network, a senate bill cosponsorship network and a dolphin network. The results show the utility of our measure for analyzing the dynamic behavior of actors and quantifying their loyalty to different timevarying affiliation groups. 1
Inhibiting the Diffusion of Contagions in BiThreshold Systems: Analytical and Experimental Results
 In Proceedings of the AAAI Fall 2011 Symposium on Complex Adaptive Systems (CASAAAI 2011
, 2011
"... We present a bithreshold model of complex contagion in networks. In this model a node in a network can be in one of two states at any time step, and changes state if enough of its neighbors are in the opposite state, as determined by “upthreshold ” and “downthreshold ” parameters. This dynamica ..."
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Cited by 2 (1 self)
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We present a bithreshold model of complex contagion in networks. In this model a node in a network can be in one of two states at any time step, and changes state if enough of its neighbors are in the opposite state, as determined by “upthreshold ” and “downthreshold ” parameters. This dynamical process models several types of social contagion processes, such as public health concerns and the spread of games on online networks. Motivated by recent literature calling for the investigation of peer pressure to reduce obesity, which can be viewed as a control problem of population dynamics, we focus on the computational complexity of finding critical sets of nodes, which are nodes that we choose to freeze in state 0 (a desirable state) in order to inhibit the spread of an undesirable state 1 in the network. We define a minimumcost critical set problem and show that it is NPcomplete for bithreshold systems. We show that several versions of the problem can be approximated to within a factor of O(logn), where n is the number of nodes in the network. Using the ideas behind these approximations, we devise a heuristic, called the Maximum Contributor Heuristic (MCH), which can be used even when the diffusion model is probabilistic. We perform simulations with wellknown networks from the literature and show that MCH outperforms the High Degree Heuristic by several orders of magnitude.
Maximizing Diffusion on Dynamic Social Networks
, 2009
"... The influence maximization problem is an important one in social network analysis, with applications from marketing to epidemiology. The task is to select some subset of the nodes in the network which, when activated, will spread the activation to the greatest portion of the rest of the network as q ..."
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The influence maximization problem is an important one in social network analysis, with applications from marketing to epidemiology. The task is to select some subset of the nodes in the network which, when activated, will spread the activation to the greatest portion of the rest of the network as quickly as possible. Since exact solutions are computationally intractable greedy approximation algorithms have been developed. However, such methods have only been tested on static social networks, or those in which the edges do not change while diffusion is occurring on the network. This is despite the fact that many social networks exhibit strongly dynamic behavior. Applying the heuristics used for static networks to dynamic ones is not straight forward, since the metrics typically used to judge the influence of nodes are not well defined when edges are changing. This paper examines the use of several potential dynamic measures for use with greedy approximation algorithms. Both linear threshold and independent cascade models of diffusion are used, and networks are formed using random, preferential attachment and proximitybased paradigms. 1 1
Working for influence: effect of network density and modularity on diffussion in networks
 ICDM DaMNet
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Dynamic behavioral mixedmembership model for large evolving networks
 In Arxiv preprint arXiv:1205.2056
, 2012
"... Abstract. The majority of realworld networks are dynamic and extremely large (e.g., Internet Traffic, Twitter, Facebook,...). To understand the structural behavior of nodes in these large dynamic networks, it may be necessary to model the dynamics of behavioral roles representing the main connec ..."
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Abstract. The majority of realworld networks are dynamic and extremely large (e.g., Internet Traffic, Twitter, Facebook,...). To understand the structural behavior of nodes in these large dynamic networks, it may be necessary to model the dynamics of behavioral roles representing the main connectivity patterns over time. In this paper, we propose a dynamic behavioral mixedmembership model (DBMM) that captures the “roles ” of nodes in the graph and how they evolve over time. Unlike other nodecentric models, our model is scalable for analyzing large dynamic networks. In addition, DBMM is flexible, parameterfree, has no functional form or parameterization, and is interpretable (identifies explainable patterns). The performance results indicate our approach can be applied to very large networks while the experimental results show that our model uncovers interesting patterns underlying the dynamics of these networks. 1
A BiThreshold Model of Complex Contagion and its Application to the Spread of Smoking Behavior
 In Proc. SNAKDD Workshop
, 2011
"... We study the dynamics of a bithreshold model of contagion, wherein each node can be in one of two states (0 or 1), and will only change state if a minimum number (specified by an upthreshold and a downthreshold at each node) of its neighbors are in the opposite state. This model applies to proces ..."
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We study the dynamics of a bithreshold model of contagion, wherein each node can be in one of two states (0 or 1), and will only change state if a minimum number (specified by an upthreshold and a downthreshold at each node) of its neighbors are in the opposite state. This model applies to processes where peer pressure is a strong factor in behavior change in either direction, such as initiation and cessation of smoking among adolescents. We investigate this model both theoretically and experimentally. On the theoretical side, we establish results which show significant differences between simple contagions (where all thresholds are 1) and complex contagions (where one or more thresholds exceed 1) with respect to the complexity of determining several global properties of the system. On the experimental side, we apply this model to the data about adolescent smoking behavior from the National Longitudinal Study of Adolescent Health (Add Health) to analyze network dynamics such as the rate of spread and outbreak size.