The journey to engaged customer community: Evidential social CRM maturity model in Twitter

We investigate the company use of Twitter as a platform for social customer relations management (SCRM) to find that message type and follower growth follow an identifiable maturity model. Studying longitudinal Twitter data from 73 Standard and Poor's companies, we find that companies map into one of two distinct maturity stages as reflected in the content of company‐generated messages. The first maturity stage consists of branding, where the focus is on customer acquisition up to a certain volume, at the expense of their engagement. The second is social care, where the focus is on retaining customers and increased engagement. We further find that companies have relatively little control over their maturity stage and that investing in social care in the first stage, aiming to increase engagement, is not effective. Proper understanding and recognition of the Twitter SCRM maturity model will allow companies to assess their methods and processes according to SCRM best practices.     

An algorithm for detecting community structure of social networks based on prior knowledge and modularity

An algorithm is proposed to detect community structure in social network. The algorithm begins with a community division based on prior knowledge of the degrees of the nodes, and then combines the communities until a clear partition is obtained. In applications such as a computer‐generated network, Ucinet networks, and Chinese rural‐urban migrants' social networks, the algorithm can achieve higher modularity and greater speed than others in the recent literature. © 2007 Wiley Periodicals, Inc. Complexity 12: 53–60, 2007     

Visualizing the “heartbeat” of a city with tweets

Describing the dynamics of a city is a crucial step to both understanding the human activity in urban environments and to planning and designing cities accordingly. Here, we describe the collective dynamics of New York City (NYC) and surrounding areas as seen through the lens of Twitter usage. In particular, we observe and quantify the patterns that emerge naturally from the hourly activities in different areas of NYC, and discuss how they can be used to understand the urban areas. Using a dataset that includes more than 6 million geolocated Twitter messages we construct a movie of the geographic density of tweets. We observe the diurnal “heartbeat” of the NYC area. The largest scale dynamics are the waking and sleeping cycle and commuting from residential communities to office areas in Manhattan. Hourly dynamics reflect the interplay of commuting, work and leisure, including whether people are preoccupied with other activities or actively using Twitter. Differences between weekday and weekend dynamics point to changes in when people wake and sleep, and engage in social activities. We show that by measuring the average distances to a central location one can quantify the weekly differences and the shift in behavior during weekends. We also identify locations and times of high Twitter activity that occur because of specific activities. These include early morning high levels of traffic as people arrive and wait at air transportation hubs, and on Sunday at the Meadowlands Sports Complex and Statue of Liberty. We analyze the role of particular individuals where they have large impacts on overall Twitter activity. Our analysis points to the opportunity to develop insight into both geographic social dynamics and attention through social media analysis. © 2015 Wiley Periodicals, Inc. Complexity 21: 280–287, 2016     

Compressing strongly connected subgroups in social networks: An entropy-based approach

To detect and study cohesive subgroups of actors is a main objective in social network analysis. What are the respective relations inside such groups and what separates them from the outside. Entropy-based analysis of network structures is an up-and-coming approach. It turns out to be a powerful instrument to detect certain forms of cohesive subgroups and to compress them to superactors without loss of information about their embeddedness in the net: Compressing strongly connected subgroups leaves the whole net’s and the (super-)actors’ information theoretical indices unchanged; i.e., such compression is information-invariant. The actual article relates on the reduction of networks with hundreds of actors. All entropy-based calculations are realized in an expert system shell.     

The effect of synchronous firing on the clustering dynamics of social amoebae

A discrete model for computer simulations of the clustering dynamics of social amoebae is presented. This model incorporates the wavelike propagation of extracellular signaling of 3′–5′‐cyclic adenosine monophosphate (cAMP), the sporadic firing of cells at early stage of aggregation, the signal relaying as a response to stimulus, and the inertia and purposeful random walk of the cell movement. It is found that the sporadic firing below the threshold of cAMP concentration plays an important role because it allows time for the cells to form synchronous firing right before the stage of aggregation, and the synchronous firing is critical for the onset of clustering behavior of social amoebae. A Monte‐Carlo simulation was also run which showed the existence of potential equilibriums of mean and variance of aggregation time. The simulation result of this model could well reproduce many phenomena observed by actual experiments. © 2013 Wiley Periodicals, Inc. Complexity 20: 16–26, 2014     

The impact of network structure on innovation efficiency: An agent‐based study in the context of innovation networks

This article investigates the impact of network structure on innovation efficiency by establishing a simulation model of innovation process in the context of innovation networks. The results indicate that short path lengths between vertices are conductive to high efficiency of explorative innovations, dense clusters are conductive to high efficiency of exploitative innovations, and high small‐worldness is conductive to high efficiency of the hybrid of these two innovations. Moreover, we discussed the reason of the results and give some suggestions to innovators and innovation policy makers. © 2014 Wiley Periodicals, Inc. Complexity 21: 111–122, 2015     

Non‐explanatory equilibria: An extremely simple game with (mostly) unattainable fixed points

Equilibrium analysis pervades mathematical social science. This paper calls into question the explanatory significance of equilibrium by offering an extremely simple game, most of whose equilibria are unattainable in principle from any of its initial conditions. Moreover, the number of computation steps required to reach those (few) equilibria that are attainable is shown to grow exponentially with the number of players—making long‐run equilibrium a poor predictor of the game's observed state. The paper also poses a number of combinatorially challenging problems raised by the game. © 2002 Wiley Periodicals, Inc.     

The effect of gossip on social networks

In this article, we develop a simple model for the effect of gossip spread on social network structure. We define gossip as information passed between two individuals A and B about a third individual C which affects the strengths of all three relationships: it strengthens A‐B and weakens both B‐C and A‐C. We find, in both an analytic derivation and model simulations, that if gossip does not spread beyond simple triads, it destroys them but if gossip propagates through large dense clusters, it strengthens them. Additionally, our simulations show that the effect of gossip on network metrics (clustering coefficient, average‐path‐length, and sum‐of‐strengths) varies with network structure and average‐node‐degree. © 2010 Wiley Periodicals, Inc. Complexity 16: 39‐47, 2011     

The bit‐economy: An artificial model of open‐ended technology discovery

We describe and demonstrate an artificial model of technology discovery called the Bit‐Economy. The model is built from a minimal set of fundamental hallmarks of technology and develops under an open‐ended evolutionary operator which rewards new technology which is able to coordinate both spatially and temporally with the existing technology set. The Bit‐Economy, is able to replicate several features of real technology development including nonmonotonic growth, bunching of creation and destruction events, qualitative topologies of patent networks, and efficiency and waste‐management gains. In contrast to related works, we do not apply an exogenous fitness landscape and so are able to study the process of technology discovery as a self‐guided search toward more complex outcomes. © 2013 Wiley Periodicals, Inc. Complexity 18: 57–67, 2013     

Riesz basis and stabilization for the flexible structure of a symmetric tree‐shaped beam network

The stabilization of a symmetric tree‐shaped network of Euler–Bernoulli beams described by a system of partial differential equations is considered. The boundary controllers are designed based on passivity principle. The eigenfrequencies are analysed in detail and the asymptotic expansion of eigenvalues are presented. It is shown that there is a set of generalized eigenfunctions for the closed‐loop system, which forms a Riesz basis with parentheses for the energy state space. This concludes the spectrum‐determined growth condition and the exponential stability of the closed‐loop system. Copyright © 2007 John Wiley & Sons, Ltd.     

Individual irrationality,network structure,and collective intelligence: An agent‐based simulation approach

An agent‐based model is constructed to study the effects of individual irrationality and network structure on collective performance. We find that individual irrationality and network density are the most influential factors. Moderate degree of irrationality results in moderate knowledge unity and superior quality of knowledge; increasing network connections or decreasing average path length (APL) can promote quality of knowledge integration and knowledge unity. Furthermore, APL is more influential than clustering coefficient and centralization (CE). Less clustering may contribute to higher quality of knowledge integration, while higher CE may contribute to higher knowledge unity. © 2015 Wiley Periodicals, Inc. Complexity 21: 44–54, 2016     

The tensor t‐function: A definition for functions of third‐order tensors

A definition for functions of multidimensional arrays is presented. The definition is valid for third‐order tensors in the tensor t‐product formalism, which regards third‐order tensors as block circulant matrices. The tensor function definition is shown to have similar properties as standard matrix function definitions in fundamental scenarios. To demonstrate the definition's potential in applications, the notion of network communicability is generalized to third‐order tensors and computed for a small‐scale example via block Krylov subspace methods for matrix functions. A complexity analysis for these methods in the context of tensors is also provided.     

Correlating temporal communication patterns of the Eclipse open source community with performance and creativity

This paper studies the temporal communication patterns of online communities of developers and users of the open source Eclipse Java development environment. It measures the productivity of each community and seeks to identify correlations that exist between group communication characteristics and productivity attributes. The study uses the TeCFlow (Temporal Communication Flow) visualizer to create movie maps of the knowledge flow by analyzing the publicly accessible Eclipse developer mailing lists as an approximation of the social networks of developers and users. Thirty-three different Eclipse communities discussing development and use of components of Eclipse such as the Java Development Tools, the different platform components, the C/C++ Development Tools and the AspectJ extension have been analyzed over a period of six months. The temporal evolution of social network variables such as betweenness centrality, density, contribution index, and degree have been computed and plotted. Productivity of each development group is measured in terms of two indices, namely performance and creativity. Performance of a group is defined as the ratio of new bugs submitted compared with bugs fixed within the same period of time. Creativity is calculated as a function of new features proposed and implemented. Preliminary results indicate that there is a correlation between attributes of social networks such as density and betweenness centrality and group productivity measures in an open source development community. We also find a positive correlation between changes over time in betweenness centrality and creativity, and a negative correlation between changes in betweenness centrality and performance.This paper was tied for Best Paper, NAACSOS (North American Association for Computational Social and Organizational Science) Annual Conference 2005, June 26–28, Notre Dame. Yared H. Kidane obtained a B.Sc. from Addis Ababa University, Ethiopia in Statistics and a M.Sc. in Information Technology specializing in engineering and management of information systems with honors from Royal Institute of Technology Stockholm, Sweden in June 2005. Yared completed his master’s thesis as an exchange student at MIT. He is currently working for Verizon Wireless as an analyst in the reporting and analysis section. Peter A. Gloor is a research fellow both at the MIT Center for Coordination Science and the Center for Digital Strategies at Tuck at Dartmouth and chief scientist at iQuest Analytics. Previously, he was a partner with Deloitte and PwC. He obtained a Ph.D. in Computer Science from the University of Zurich in 1989, and was a Post-Doc at the MIT Lab for Computer Science.     

The complexity of advice‐giving

Advice‐giving about personal problems is a common form of human interaction. However, an open question is whether there is an abstract and general logic that explains how advice‐giving works. In this study, we addressed this question from the perspective of dynamical systems. We measured the nonlinear dynamics of advice‐giving by using recurrence quantification analysis. Analyzing 600 texts of request for advice and the advice given, our results uncover a typical logic of advice‐giving, and suggest that advice‐giving may be understood as a dynamic manipulation of perspective‐taking. © 2009 Wiley Periodicals, Inc. Complexity 2009     

Network analysis: Understanding consumers' choice in the film industry and predicting pre‐released weekly box‐office revenue

Predicting weekly box‐office demand is an important yet challenging question. For theater exhibitors, such information will enhance negotiation options with distributers, and assist in planning weekly movie portfolio mix. Existing literature focuses on forecasts of pre‐released total gross revenue or on weekly predictions based on first‐weeks observations. This work adds to the literature in modeling the entire demand structure forecasts by utilizing information on movie similarity network. Specifically, we draw upon the assumption that aggregated consumers' choice in the film industry is the main key in understanding movies' demand. Therefore, similar movies, in terms of audience appeal, should yield similar demand structure. In this work, we propose an automated technique that derives measurements of demand structure. We demonstrate that our technique enables to analyze different aspects of demand structure, namely, decay rate, time of first demand peak, per‐screen gross value at peak time, existence of second demand wave, and time on screens. We deploy ideas from variable selection procedures, to investigate the prediction power of similarity network on demand dynamics. We show that not only our models perform significantly better than models that discard the similarity network but are also robust to new sets of box‐office movies. Copyright © 2016 John Wiley & Sons, Ltd.     

Studying the emerging global brain: Analyzing and visualizing the impact of co‐authorship teams

This article introduces a suite of approaches and measures to study the impact of co‐authorship teams based on the number of publications and their citations on a local and global scale. In particular, we present a novel weighted graph representation that encodes coupled author‐paper networks as a weighted co‐authorship graph. This weighted graph representation is applied to a dataset that captures the emergence of a new field of science and comprises 614 articles published by 1036 unique authors between 1974 and 2004. To characterize the properties and evolution of this field, we first use four different measures of centrality to identify the impact of authors. A global statistical analysis is performed to characterize the distribution of paper production and paper citations and its correlation with the co‐authorship team size. The size of co‐authorship clusters over time is examined. Finally, a novel local, author‐centered measure based on entropy is applied to determine the global evolution of the field and the identification of the contribution of a single author's impact across all of its co‐authorship relations. A visualization of the growth of the weighted co‐author network, and the results obtained from the statistical analysis indicate a drift toward a more cooperative, global collaboration process as the main drive in the production of scientific knowledge. © 2005 Wiley Periodicals, Inc. Complexity 10: 57–67, 2005     

基于面板数据的伊犁州直县(市)居民消费水平的聚类分析研究

运用多元统计分析中聚类分析的方法,对新疆伊犁州直十个县(市)2012年社会综合消费水平的数据进行了比较分析,综合评价了各地区的居民消费水平,并根据得到的分类情况提出了相应的改进意见,使之进一步改善和提高.     

The Social Embeddedness of Academic Online Groups in Offline Networks as a Norm Generating Structure: An Empirical Test of the Coleman Model on Norm Emergence

This paper analyzes two questions. First, under which conditions does a norm emerge in academic online groups that prescribes members to help others during group discussions? Second, what effects does such a norm, and other social conditions, have on the contributing behavior of researchers during online discussions? It is argued that the Coleman model (1990) on the emergence of norms points to an important condition that facilitates the realization of such a norm. According to the Coleman model (1990) a dense network among members of a group tends to strengthen a group norm. The paper makes a distinction between different kinds of academic online groups. The criterion of the distinction is the extent to which within the membership a highly integrated research community exists. An online group with a highly integrated research community is called to have a high degree of social embeddedness of its online communication in offline networks. It is hypothesized that a high degree of embeddedness has a number of effects. A higher degree of embeddedness leads to a stronger help-prescribing norm. The stronger the norm the more researchers send online answers to questions of their co-members during public online discussions. Furthermore, a high degree of embeddedness increases the answering behavior of researchers directly because it provides opportunities to gain reputation within the academic community through contributing to the discussion. The study makes use of data that consist of a combination of survey data and observed data of the communication behavior of researchers in about 50 international academic emailing lists. The results provide evidence for the expected effect of embeddedness on the strength of the norm and for the effect of embeddedness on the answering behavior of researchers. The strength of the help-prescribing norm indirectly influences the answering behavior.