Crowdsourcing is an emerging tool for collaboration and innovation platforms. Recently, crowdsourcing platforms have become a vital tool for firms to generate new ideas, especially large firms such as Dell, Microsoft, and Starbucks, Crowdsourcing provides firms with multiple advantages, notably, rapid solutions, cost savings, and a variety of novel ideas that represent the diversity inherent within a crowd. The literature on crowdsourcing is limited to empirical evidence of the advantage of crowdsourcing for businesses as an innovation strategy. In this study, Starbucks’ crowdsourcing platform, Ideas Starbucks, is examined, with three objectives: first, to determine crowdsourcing participants’ perception of the company by crowdsourcing participants when generating ideas on the platform. The second objective is to map users into a community structure to identify those more likely to produce ideas; the most promising users are grouped into the communities more likely to generate the best ideas. The third is to study the relationship between the users’ ideas’ sentiment scores and the frequency of discussions among crowdsourcing users. The results indicate that sentiment and emotion scores can be used to visualize the social interaction narrative over time. They also suggest that the fast greedy algorithm is the one best suited for community structure with a modularity on agreeable ideas of 0.53 and 8 significant communities using sentiment scores as edge weights. For disagreeable ideas, the modularity is 0.47 with 8 significant communities without edge weights. There is also a statistically significant quadratic relationship between the sentiments scores and the number of conversations between users.

The ability for people and organizations to connect in the digital age has allowed the growth of networks that cover an increasing proportion of human interactions. The research community investigating networks asks a range of questions such as which participants are most central, and which community label to apply to each member. This paper deals with the question on how to label nodes based on the features (attributes) they contain, and then how to model the changes in the label assignments based on the influence they produce and receive in their networked neighborhood. The methodological approach applies the simple graph convolutional neural network in a novel setting. Primarily that it can be used not only for label classification, but also for modeling the spread of the influence of nodes in the neighborhoods based on the length of the walks considered. This is done by noticing a common feature in the formulations in methods that describe information diffusion which rely upon adjacency matrix powers and that of graph neural networks. Examples are provided to demonstrate the ability for this model to aggregate feature information from nodes based on a parameter regulating the range of node influence which can simulate a process of exchanges in a manner which bypasses computationally intensive stochastic simulations.

The k-clique problem is identifying the largest complete subgraph of size k on a network, and it has many applications in Social Network Analysis (SNA), coding theory, geometry, etc. Due to the NP-Complete nature of the problem, the meta-heuristic approaches have raised the interest of the researchers and some algorithms are developed. In this paper, a new algorithm based on the Bat optimization approach is developed for finding the maximum k-clique on a social network to increase the convergence speed and evaluation criteria such as Precision, Recall, and F1-score. The proposed algorithm is simulated in Matlab® software over Dolphin social network and DIMACS dataset for k = 3, 4, 5. The computational results show that the convergence speed on the former dataset is increased in comparison with the Genetic Algorithm (GA) and Ant Colony Optimization (ACO) approaches. Besides, the evaluation criteria are also modified on the latter dataset and the F1-score is obtained as 100% for k = 5.

Centrality measures have been proved to be a salient computational science tool for analyzing networks in the last two to three decades aiding many problems in the domain of computer science, economics, physics, and sociology. With increasing complexity and vividness in the network analysis problems, there is a need to modify the existing traditional centrality measures. Weighted centrality measures usually consider weights on the edges and assume the weights on the nodes to be uniform. One of the main reasons for this assumption is the hardness and challenges in mapping the nodes to their corresponding weights. In this paper, we propose a way to overcome this kind of limitation by hybridization of the traditional centrality measures. The hybridization is done by taking one of the centrality measures as a mapping function to generate weights on the nodes and then using the node weights in other centrality measures for better complex ranking.

The issue of polarization in online social media has been gaining attention in recent years amid the changing political landscapes of many parts of the world. Several studies empirically observed the existence of echo chambers in online social media, stimulating a slew of works that tries to model the phenomenon via opinion modeling. Here, we propose a model of opinion dynamics centered around the notion that opinion changes are invoked by news exposure. Our model comes with parameters for opinions and connection strength which are updated through news propagation. We simulate the propagation of multiple news under the model in synthetic networks and observe the evolution of the model’s parameters and the propagation structure induced. Unlike previous models, our model successfully exhibited not only polarization of opinion, but also segregated propagation structure. By analyzing the results of our simulations, we found that the formation probability of echo chambers is primarily connected to the news polarization. However, it is also affected by intolerance to dissimilar opinions and how quickly individuals update their opinions. Through simulations on Twitter networks, we found that the behavior of the model is reproducible across different network structure and sizes.

In this paper, we address privacy issues related to ranked retrieval model in web databases, each of which takes private attributes as part of input in the ranking function. Many web databases keep private attributes invisible to public and believe that the adversary is unable to reveal the private attribute values from query results. However, prior research (Rahman et al. in Proc VLDB Endow 8:1106–17, 2015) studied the problem of rank-based inference of private attributes over web databases. They found that one can infer the value of private attributes of a victim tuple by issuing well-designed queries through a top-k query interface. To address the privacy issue, in this paper, we propose a novel privacy-preserving framework. Our framework protects private attributes’ privacy not only under inference attacks but also under arbitrary attack methods. In particular, we classify adversaries into two widely existing categories: domain-ignorant and domain-expert adversaries. Then, we develop equivalent set with virtual tuples (ESVT) for domain-ignorant adversaries and equivalent set with true tuples (ESTT) for domain-expert adversaries. The ESVT and the ESTT are the primary parts of our privacy-preserving framework. To evaluate the performance, we define a measurement of privacy guarantee for private attributes and measurements for utility loss. We prove that both ESVT and ESTT achieve the privacy guarantee. We also develop heuristic algorithms for ESVT and ESTT, respectively, under the consideration of minimizing utility loss. We demonstrate the effectiveness of our techniques through theoretical analysis and extensive experiments over real-world dataset.

Network equivalence is a technique useful for many areas including power systems. In many power system analyses, generation shift factor (GSF)-based bus clustering methods have been widely used to reduce the complexity of the equivalencing problem. GSF captures power flow on a line when power is injected at a node using bus to bus electrical distance. A more appropriate measure is the one which captures what may be called the electrical line distance with respect to a bus termed as relative bus to line distance. With increase in power transactions across different regions, the use of relative bus to line distance becomes appropriate for many analyses. Inspired by the recent trends in network science on the study of network dynamics based on the topological characteristics of a network, in this paper, we present a bus clustering method based on average electrical distance (AED). AED is independent of changes in location of slack bus and is based on the concept of electrical distance introduced in the context of molecular chemistry and pursued later for applications in social and complex networks. AED represents the AED from a bus to buses of the transmission line of interest. We first propose an AED-based method to group the buses into clusters for power systems network equivalence using k-means clustering algorithm integrated with silhouette analysis. One limitation of this method is that despite its speed, sometimes it may yield clusters of inferior quality compared to the optimal solution. To overcome this limitation, we next present our improved clustering method which incorporates a seeding technique that initializes centroids probabilistically. We also incorporate a technique in our method to find the number of clusters, k, to be given as input to our clustering algorithm. The resulting algorithm called AED-based k-means++ clustering method yields a clustering that is O(logk) competitive. Our network equivalence technique is next described. Finally, the efficacy of our new equivalencing technique is demonstrated by evaluating its performance on the IEEE 300-bus system and comparing that to the performance of our AED-based method (Sharma et al. in Power network equivalents: a network science-based k-means clustering method integrated with silhouette analysis. In: Complex networks and their application VI, Lyon, France. p. 78–89, 2017) and the existing GSF-based method.

BackgroundIn this paper, we study the process of opinion dynamics and consensus building in online collaboration systems, in which users interact with each other following their common interests and their social profiles. Specifically, we are interested in how users similarity and their social status in the community, as well as the interplay of those two factors, influence the process of consensus dynamics.MethodsFor our study, we simulate the diffusion of opinions in collaboration systems using the well-known Naming Game model, which we extend by incorporating an interaction mechanism based on user similarity and user social status. We conduct our experiments on collaborative datasets extracted from the Web.ResultsOur findings reveal that when users are guided by their similarity to other users, the process of consensus building in online collaboration systems is delayed. A suitable increase of influence of user social status on their actions can in turn facilitate this process.ConclusionsIn summary, our results suggest that achieving an optimal consensus building process in collaboration systems requires an appropriate balance between those two factors.

In this paper, we model the problem of influencing the opinions of groups of individuals as a containment control problem, as in many practical scenarios, the control goal is not full consensus among all the individual opinions, but rather their containment in a certain range, determined by a set of leaders. As in classical bounded confidence models, we consider individuals affected by the confirmation bias, thus tending to influence and to be influenced only if their opinions are sufficiently close. However, here we assume that the confidence level, modeled as a proximity threshold, is not constant and uniform across the individuals, as it depends on their opinions. Specifically, in an extremist society, the most radical agents (i.e., those with the most extreme opinions) have a higher appeal and are capable of influencing nodes with very diverse opinions. The opposite happens in a moderate society, where the more connected (i.e., influential) nodes are those with an average opinion. In three artificial societies, characterized by different levels of extremism, we test through extensive simulations the effectiveness of three alternative containment strategies, where leaders have to select the set of followers they try to directly influence. We found that, when the network size is small, a stochastic time-varying pinning strategy that does not rely on information on the network topology proves to be more effective than static strategies where this information is leveraged, while the opposite happens for large networks where the relevance of the topological information is prevalent.

BackgroundNetwork-Oriented Modelling based on adaptive temporal–causal networks provides a unified approach to model and analyse dynamics and adaptivity of various processes, including mental and social interaction processes.MethodsAdaptive temporal–causal network models are based on causal relations by which the states in the network change over time, and these causal relations are adaptive in the sense that they themselves also change over time.ResultsIt is discussed how modelling and analysis of the dynamics of the behaviour of these adaptive network models can be performed. The approach is illustrated for adaptive network models describing social interaction.ConclusionsIn particular, the homophily principle and the ‘more becomes more’ principles for social interactions are addressed. It is shown how the chosen Network-Oriented Modelling method provides a basis to model and analyse these social phenomena.