Rapid Information Propagation Research Articles

Influential Node Detection and Ranking With Fusion of Heterogeneous Social Media Information

Identification of influential nodes has emerged as one of the major challenges, especially after their use in the rapid propagation of information, epidemics, and so on in social media. Most of the previous works in this field deal with the homogeneous interactions that are not pertinent in the determination of the accurate context of the nodes due to their noisy and sparse nature. Hence, heterogeneous interactions need to be explored for the identification of influential nodes in the network. To consider heterogeneous interactions available within the network, a multilayer network (ML) has been designed in this work. Each layer of the network represents a particular type of interaction, e.g., upload, comment, retweet, reply, and mention. A heterogeneous degree ranking (HDR)-based influential nodes’ detection and ranking are proposed for the designed ML. Furthermore, multiple-criteria decision-making (MCDM) methods, such as the analytic hierarchy process (AHP), the technique for order of preference by similarity to ideal solution (TOPSIS), fuzzy AHP, fuzzy TOPSIS, and the analytic network process (ANP), are explored for the proposed ML for identification and ranking of the influential nodes. The susceptible–infected–recovered (SIR) model is used to evaluate the proposed work. In addition to this, statistical analysis is performed using the Pearson correlation, Kendall’s correlation, Spearman’s correlation, and the Friedman test on the ranks generated by different methods, which shows that the results generated by different proposed methods are consistent. Furthermore, the performance of the proposed method is compared with state-of-the-art approaches.

Hive geometry shapes the recruitment rate of honeybee colonies.

Honey bees make decisions regarding foraging and nest-site selection in groups ranging from hundreds to thousands of individuals. To effectively make these decisions, bees need to communicate within a spatially distributed group. However, the spatiotemporal dynamics of honey bee communication have been mostly overlooked in models of collective decisions, focusing primarily on mean field models of opinion dynamics. We analyze how the spatial properties of the nest or hive, and the movement of individuals with different belief states (uncommitted or committed) therein affect the rate of information transmission using spatially-extended models of collective decision-making within a hive. Honeybees waggle-dance to recruit conspecifics with an intensity that is a threshold nonlinear function of the waggler concentration. Our models range from treating the hive as a chain of discrete patches to a continuous line (long narrow hive). The combination of population-thresholded recruitment and compartmentalized populations generates tradeoffs between rapid information propagation with strong population dispersal and recruitment failures resulting from excessive population diffusion and also creates an effective colony-level signal-detection mechanism whereby recruitment to low quality objectives is blocked.

Fluid Approximation–based Analysis for Mode-switching Population Dynamics

Fluid approximation results provide powerful methods for scalable analysis of models of population dynamics with large numbers of discrete states and have seen wide-ranging applications in modelling biological and computer-based systems and model checking. However, the applicability of these methods relies on assumptions that are not easily met in a number of modelling scenarios. This article focuses on one particular class of scenarios in which rapid information propagation in the system is considered. In particular, we study the case where changes in population dynamics are induced by information about the environment being communicated between components of the population via broadcast communication. We see how existing hybrid fluid limit results, resulting in piecewise deterministic Markov processes, can be adapted to such models. Finally, we propose heuristic constructions for extracting the mean behaviour from the resulting approximations without the need to simulate individual trajectories.

Social QA in non-CQA platforms

Community Question Answering (cQA) sites have emerged as platforms designed specifically for the exchange of questions and answers among communities of users. Although users tend to find good quality answers in cQA sites, there is evidence that they also engage in a significant volume of QA in other types of social sites, such as microblog platforms. Research indicates that users opt for these non-specific QA social networks because they contain up-to-date information on current events, also due to their rapid information propagation, and social trust. In this sense, we propose that microblog platforms can emerge as a novel, valuable source of information for QA information retrieval tasks. However, we have found that it is not straightforward to transfer existing approaches for automatically retrieving relevant answers in traditional cQA platforms for use in microblogs. This occurs because there are unique characteristics that differentiate microblog data from that of traditional cQA, such as noise and very short text length. In this work, we study (1) if microblog data can be used to automatically provide relevant answers for the QA task, and, in addition, (2) which features contribute the most for finding relevant answers for a particular query. In particular, we introduce a conversation (thread)-level document model, as well as a machine learning ranking framework for microblog QA. We validate our proposal by using factoid-QA as a proxy task, showing that Twitter conversations can indeed be used to automatically provide relevant results for QA. We are able to identify the importance of different features that contribute the most for QA ranking. In addition, we provide evidence that our method allows us to retrieve complex answers in the domain of non-factoid questions.

Fast and Accurate Depth Estimation from Sparse Light Fields.

We present a fast and accurate method for dense depth reconstruction, which is specifically tailored to process sparse, wide-baseline light field data captured with camera arrays. In our method, the source images are over-segmented into non-overlapping compact superpixels. We model superpixel as planar patches in the image space and use them as basic primitives for depth estimation. Such superpixel-based representation yields desired reduction in both memory and computation requirements while preserving image geometry with respect to the object contours. The initial depth maps, obtained by plane-sweeping independently for each view, are jointly refined via iterative belief-propagation-like optimization in superpixel domain. During the optimization, smoothness between the neighboring superpixels and geometric consistency between the views are enforced. To ensure rapid information propagation into textureless and occluded regions, together with the immediate superpixel neighbors, candidates from larger neighborhoods are sampled. Additionally, in order to make full use of the parallel graphics hardware a synchronous message update schedule is employed allowing to process all the superpixels of all the images at once. This way, the distribution of the scene geometry becomes distinctive already after the first iterations, facilitating stability and fast convergence of the refinement procedure. We demonstrate that a few refinement iterations result in globally consistent dense depth maps even in the presence of wide textureless regions and occlusions. The experiments show that while the depth reconstruction takes about a second per full high-definition view, the accuracy of the obtained depth maps is comparable with the state-of-the-art results, which otherwise require much longer processing time.

Mutual clustering coefficient-based suspicious-link detection approach for online social networks

Online social networks (OSNs) are trendy and rapid information propagation medium on the web where millions of new connections either positive such as acquaintance, or negative such as animosity, are being established every day around the world. The negative links (or sometimes referred to as harmful connections) are mostly established by fake profiles as they are being created by minds with ill aims. Detecting negative (or suspicious) links within online users can better aid in the mitigation of fake profiles from OSNs.A modified clustering coefficient formula, named as MutualClusteringCoefficient represented byMcc, is introduced to quantitatively measure the connectivity between the mutual friends of two connected users in a group. In this paper, a classification system based on mutual clustering coefficient and profile information of users has been presented to detect the suspicious links within the user communities. Profile information helps us to find the similarity between users. Different similarity measures have been employed to calculate the profile similarity between a connected user pair. Experimental results demonstrate that four basic and easily available features such as workw,educatione,home_townhtandcurrent_city(cc) along with MCC play a vital role in designing a successful classification system for the detection of suspicious links.

Stany świadomości w świetle neuronauk

The human brain can be modelled as a complex network, and may have a small-world structure both at the level of anatomical as well as functional connectivity (optimal organization of different brain network associated with rapid information propagation, minimal wiring costs, as well as a balance between local processing and global integration) in which activity of Default Mode Network play important role in different states of consciousness. The small-world structure deviation reflects genuine changes in levels of consciousness: 1) coma state: increased activity of precuneus and posterior cingulate cortex and domination alpha band over theta and delta band connected with low activity of alpha, theta, and delta band, 2) vegetative state: increased connectivity in Default Mode Network and increased power in delta, theta, beta, and low alpha band, 3) state of minimal consciousness: decreased activation of the Default Mode Network during tasks, domination alpha band over theta and delta band connected with high activity of alpha, theta, and delta band, 4) meditation state: reorganization of central and peripheral elements of the brain network, changes in quantity, quality, orientation of longitudinal (antero-posterior) and transverse (right-left) edges, and its frequency of activity, increased longitudinal integration and hemispherical synchronization. Future neuroimaging investigations are needed. Especially the new sophisticated spectral metrics could be potentially effective in clinical setting and have diagnostic, therapeutic and prognostic relevance.

Network structure optimization algorithm for information propagation considering edge clustering and diffusion characteristics

Optimizing network structure to promote information propagation has been a key issue in the research field of complex network, and both clustering and diffusion characteristics of edges in a network play a very important role in information propagation. K-truss decomposition is an algorithm for identifying the most influential nodes in the network. We find that K-truss decomposition only considers edge clustering characteristics, without considering the diffusion characteristics, so it is easily affected by the local clustering structure in the network, such as core-like groups. There are mutually closely connected the core-like groups in the network, but the correlation between the core-like groups and the other parts of the network is less, so the information is easy to spread in the core-like groups, but not in the other parts of the network, nor over the whole network. For the reason, we propose an index to measure the edge diffusion characteristics in a network, and it is found that the diffusion characteristics of some edges in the periphery of the network are relatively high, but the clustering characteristics of these edges are relatively low, so they are not beneficial for rapid information propagation. In this paper, by considering the relationship between the clustering characteristics and diffusion characteristics of the edges, we propose a novel network structure optimization algorithm for information propagation. By measuring the comprehensive ability strength of the clustering characteristics and the diffusion characteristics of the edges, we can filter out the edges whose comprehensive ability is poor in the network, then determine whether the edges should be optimized according to the relative relationship between the clustering characteristics and the diffusion characteristics of the edges. To prove the effectiveness of the proposed algorithm, it is carried out to optimize the structures of four real networks, and verify the effective range of information propagation before and after the optimization of network structure from the classical independent cascade model. The results show that the network topology optimized by the proposed algorithm can effectively increase the range of information propagation. Moreover, the number of leaf nodes in the optimized network is reduced, and the clustering coefficient and the average path length are also reduced.

ActiveDBC: learning Knowledge-based Information propagation in mobile social networks

Due to fast-growing mobile devices usage such as smartphones and wearable devices, the rapid information propagation in the Mobile Social Networks environment is very important. In particular, information transmission of people with repeated daily patterns in complex areas such as big cities requires a very meaningful analysis. We address the problem of identifying a key player who can quickly propagate the information to the whole network. This problem, in other words, often refer as the information propagation problem. In this research, we selected the top-k influential nodes to learn the knowledge-based movements of people by using a Markov chain process in a real-life environment. Subsequently, their movement probabilities according to virtual regions were used to ensure appropriate clustering based on the Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. Since moving patterns in a university campus data have a dense collection of people, the DBSCAN algorithm was useful for producing very dense groupings. After clustering, we also elected the top-k influential nodes based on the results learned from the score of each node according to groups. We determined the rate at which information spreads by using trace data from a real network. Our experiments were conducted in the Opportunistic Network Environment simulator. The results showed that the proposed method has outstanding performance for the level of spreading time in comparison to other methods such as Naive, Degree, and K-means. Furthermore, we compared the performance of RandomDBC with that of ActiveDBC, proving that the latter method was important to extract the influential top-k nodes, and showed superior performance.

Institutional vs. Non-institutional use of Social Media during Emergency Response: A Case of Twitter in 2014 Australian Bush Fire

Social media plays a significant role in rapid propagation of information when disasters occur. Among the four phases of disaster management life cycle: prevention, preparedness, response, and recovery, this paper focuses on the use of social media during the response phase. It empirically examines the use of microblogging platforms by Emergency Response Organisations (EROs) during extreme natural events, and distinguishes the use of Twitter by EROs from digital volunteers during a fire hazard occurred in Australia state of Victoria in early February 2014. We analysed 7982 tweets on this event. While traditionally theories such as World System Theory and Institutional Theory focus on the role of powerful institutional information outlets, we found that platforms like Twitter challenge such notion by sharing the power between large institutional (e.g. EROs) and smaller non-institutional players (e.g. digital volunteers) in the dissemination of disaster information. Our results highlight that both large EROs and individual digital volunteers proactively used Twitter to disseminate and distribute fire related information. We also found that the contents of tweets were more informative than directive, and that while the total number of messages posted by top EROs was higher than the non-institutional ones, non-institutions presented a greater number of retweets.

Fake profile detection techniques in large-scale online social networks: A comprehensive review

In the present era, online social networks are the most popular and rapid information propagation applications on the Internet. People of all ages spend most of their time on social networking sites. Huge volumes of data are being created and shared through social networks around the world. These interests have given rise to illegitimate users who engage in fraudulent activities against social network users. On social networks, fake profile creation is considered to cause more harm than any other form of cyber crime. This crime has to be detected even before the user is notified about the fake profile creation. Many algorithms and methods, most of which use the huge volume of unstructured data generated from social networks, have been proposed for the detection of fake profiles. This study presents a survey of the existing and latest technical work on fake profile detection.

On information propagation in mobile call networks

We consider the dynamics of rapid propagation of information (RPI) in mobile phone networks. We propose a heuristic method for identification of sequences of calls that supposedly propagate the same information and apply it to large-scale real-world data. We show that some of the information propagation events identified by the proposed method can explain the physical co-location of subscribers. We further show that features of subscriber’s behavior in these events can be used for efficient churn prediction. To the best of our knowledge, our method for churn prediction is the first method that relies on dynamic, rather than static, social behavior. Finally, we introduce two generative models that address different aspects of RPI. One model describes the emergence of sequences of calls that lead to RPI. The other model describes the emergence of different topologies of paths in which the information propagates from one subscriber to another. We report high correspondence between certain features observed in the data and these models.

The World of Connections and Information Flow in Twitter

Information propagation in online social networks like Twitter is unique in that word-of-mouth propagation and traditional media sources coexist. We collect a large amount of data from Twitter to compare the relative roles different types of users play in information flow. Using empirical data on the spread of news about major international headlines as well as minor topics, we investigate the relative roles of three types of information spreaders: 1) mass media sources like BBC; 2) grassroots, consisting of ordinary users; and 3) evangelists, consisting of opinion leaders, politicians, celebrities, and local businesses. Mass media sources play a vital role in reaching the majority of the audience in any major topics. Evangelists, however, introduce both major and minor topics to audiences who are further away from the core of the network and would otherwise be unreachable. Grassroots users are relatively passive in helping spread the news, although they account for the 98% of the network. Our results bring insights into what contributes to rapid information propagation at different levels of topic popularity, which we believe are useful to the designers of social search and recommendation engines.