Efficiency Of Information Diffusion Research Articles

Diagnosis of Glioblastoma Multiforme Progression via Interpretable Structure-Constrained Graph Neural Networks.

Glioblastoma multiforme (GBM) is the most common type of brain tumors with high recurrence and mortality rates. After chemotherapy treatment, GBM patients still show a high rate of differentiating pseudoprogression (PsP), which is often confused as true tumor progression (TTP) due to high phenotypical similarities. Thus, it is crucial to construct an automated diagnosis model for differentiating between these two types of glioma progression. However, attaining this goal is impeded by the limited data availability and the high demand for interpretability in clinical settings. In this work, we propose an interpretable structure-constrained graph neural network (ISGNN) with enhanced features to automatically discriminate between PsP and TTP. This network employs a metric-based meta-learning strategy to aggregate class-specific graph nodes, focus on meta-tasks associated with various small graphs, thus improving the classification performance on small-scale datasets. Specifically, a node feature enhancement module is proposed to account for the relative importance of node features and enhance their distinguishability through inductive learning. A graph generation constraint module enables learning reasonable graph structures to improve the efficiency of information diffusion while avoiding propagation errors. Furthermore, model interpretability can be naturally enhanced based on the learned node features and graph structures that are closely related to the classification results. Comprehensive experimental evaluation of our method demonstrated excellent interpretable results in the diagnosis of glioma progression. In general, our work provides a novel systematic GNN approach for dealing with data scarcity and enhancing decision interpretability. Our source codes will be released at https://github.com/SJTUBME-QianLab/GBM-GNN.

Social network analysis of project procurement in Iranian construction mega projects

Several construction projects, worldwide, are suffering from delays and budget overruns; and Western Asia is not an exception. Studies have identified delays in progress payments and change order approvals as two of the main root causes of overruns in large building projects in the Middle East. In this study, the effect of procurement methods on these two factors has been investigated by analyzing the structure of social networks formed during these processes and evaluating the efficiency of information diffusion in such networks. Three contract types are focused, i.e., unit-rate; lump-sum; and EPC (Engineering, Procurement and Construction) to evaluate the functional performance of actor networks associated with them, and social network analysis (SNA) is used as the main analysis tool. After extracting the social networks formed in the mega-projects from the public domain in the Iranian construction industry, metrics from SNA were used to evaluate their functional performance concerning progress payment and change order management. The processes were compared at both network-level and the individuals involved. The observations were turned into an industry questionnaire and the key stakeholders involved in the projects were surveyed for validation and additional insights. The results identified the actors/roles of the most prominence in change and invoice networks of projects, as well as the strength and weaknesses of the three forms of standard contracts reviewed, as manifested in the performance of invoice and change management processes. It was observed that the formation of flat actor-networks in more modern contracts (such as the EPC) effectively supports a better flow of information, hence improves the performance of the processes under the study.

Network modularity controls the speed of information diffusion.

The rapid diffusion of information and the adoption of social behaviors are of critical importance in situations as diverse as collective actions, pandemic prevention, or advertising and marketing. Although the dynamics of large cascades have been extensively studied in various contexts, few have systematically examined the impact of network topology on the efficiency of information diffusion. Here, by employing the linear threshold model on networks with communities, we demonstrate that a prominent network feature-the modular structure-strongly affects the speed of information diffusion in complex contagion. Our simulations show that there always exists an optimal network modularity for the most efficient spreading process. Beyond this critical value, either a stronger or a weaker modular structure actually hinders the diffusion speed. These results are confirmed by an analytical approximation. We further demonstrate that the optimal modularity varies with both the seed size and the target cascade size and is ultimately dependent on the network under investigation. We underscore the importance of our findings in applications from marketing to epidemiology, from neuroscience to engineering, where the understanding of the structural design of complex systems focuses on the efficiency of information propagation.

Efficient network seeding under variable node cost and limited budget for social networks

The efficiency of information diffusion on networks highly depends on both the network structure and the set of early spreaders. Moreover, in various realistic scenarios, to seed different nodes implies different costs, as in the case of viral marketing, where costs often correlate with local network structure. The budgeted influence maximization (BIM) problem consists in determining a seed set whose diffusion maximizes the total number of influenced nodes, provided that the seeding cost is within a given budget. We investigate efficient seeding strategies for the BIM problem under the deterministic fixed threshold diffusion model. In particular, we introduce the concept of surrounding sets: relatively cheap seeds neighboring expensive, structurally-privileged nodes, which then become spreaders at lower costs. Numerical experiments with several real networks indicate our method outperforms strategies that seed nodes based on their influence/cost ratios. A key insight from our evaluation is that larger diffusion is generally attained from the surrounding sets that consider the two-hop neighborhood of influential nodes, as opposed to their immediate neighbors only.

Robustness and efficiency in interconnected networks with changes in network assortativity

In this study, the effect of assortativity on the robustness and efficiency of interconnected networks was investigated. This involved constructing a network that possessed the desired degree of assortativity. Additionally, an interconnected network was constructed wherein the assortativity between component networks possessed the desired value. With respect to single networks, the results indicated that a decrease in assortativity provided low hop length, high information diffusion efficiency, and distribution of communication load on edges. The study also revealed that excessive assortativity led to poor network performance. In the study, the assortativity between networks was defined and the following results were demonstrated: assortative connections between networks lowered the average hop length and enhanced information diffusion efficiency, whereas disassortative connections between networks distributed the communication loads of internetwork links and enhanced robustness. Furthermore, it is necessary to carefully adjust assortativity based on the node degree distribution of networks. Finally, the application of the results to the design of robust and efficient information networks was discussed.

Revealing the efficiency of information diffusion in online social networks of microblog

Online Social Networks (OSNs) such as microblogs enable people to simultaneously share information with a large number of peers, and revealing the properties of the formed network structure and the corresponding impacts on information diffusion are important. In this article, we empirically study 10millions user profiles from the largest Chinese mircoblog, Sina Weibo, and 41.7millions profiles from Twitter. By studying followers’ topology, we find an invariant characteristic that the followers count of users obeys a power law distribution with exponent near 2. We then investigate the reason from the theoretical view and find this network structure achieves the optimal information diffusion in terms of inducing a minimal propagation times. Our study thus reveals the efficiency of information diffusion in OSNs of microblogs, which is validated by experimental examination, theoretical analysis and numerical simulation.

Modeling and performance analysis of information diffusion under information overload in Facebook‐like social networks

SUMMARYResearch on social networks has received remarkable attention, because an increasing number of people use social networks to broadcast information and stay connected with their friends. However, because of the information overload in social networks, it becomes increasingly difficult for users to find useful information. This paper takes Facebook‐like social networks into account and proposes models to capture the characters such as the network, the user behaviors, and the process of information diffusion under information overload. The term type influence is introduced to characterize the information diffusion efficiency for users of a given type, which can be analyzed theoretically on the basis of the proposed models. Having noticed the inaccuracy of using type influence to estimate the information diffusion efficiency for a given user, we further introduce the term individual influence and propose a scalable approach to estimate it. We verify the accuracy of this approach by simulations and show that considering more nearby users leads to more computational costs, but more accurate results. Copyright © 2014 John Wiley & Sons, Ltd.

Modeling of information diffusion in Twitter-like social networks under information overload.

Due to the existence of information overload in social networks, it becomes increasingly difficult for users to find useful information according to their interests. This paper takes Twitter-like social networks into account and proposes models to characterize the process of information diffusion under information overload. Users are classified into different types according to their in-degrees and out-degrees, and user behaviors are generalized into two categories: generating and forwarding. View scope is introduced to model the user information-processing capability under information overload, and the average number of times a message appears in view scopes after it is generated by a given type user is adopted to characterize the information diffusion efficiency, which is calculated theoretically. To verify the accuracy of theoretical analysis results, we conduct simulations and provide the simulation results, which are consistent with the theoretical analysis results perfectly. These results are of importance to understand the diffusion dynamics in social networks, and this analysis framework can be extended to consider more realistic situations.

INFORMATION DIFFUSION IN FACEBOOK-LIKE SOCIAL NETWORKS UNDER INFORMATION OVERLOAD

Research on social networks has received remarkable attention, since many people use social networks to broadcast information and stay connected with their friends. However, due to the information overload in social networks, it becomes increasingly difficult for users to find useful information. This paper takes Facebook-like social networks into account, and models the process of information diffusion under information overload. The term view scope is introduced to model the user information-processing capability under information overload, and the average number of times a message appears in view scopes after it is generated is proposed to characterize the information diffusion efficiency. Through theoretical analysis, we find that factors such as network structure and view scope number have no impact on the information diffusion efficiency, which is a surprising result. To verify the results, we conduct simulations and provide the simulation results, which are consistent with the theoretical analysis results perfectly.

Skeleton of weighted social network

In the literature of social networks, understanding topological structure is an important scientific issue. In this paper, we construct a network from mobile phone call records and use the cumulative number of calls as a measure of the weight of a social tie. We extract skeletons from the weighted social network on the basis of the weights of ties, and we study their properties. We find that strong ties can support the skeleton in the network by studying the percolation characters. We explore the centrality of w-skeletons based on the correlation between some centrality measures and the skeleton index w of a vertex, and we find that the average centrality of a w-skeleton increases as w increases. We also study the cumulative degree distribution of the successive w-skeletons and find that as w increases, the w-skeleton tends to become more self-similar. Furthermore, fractal characteristics appear in higher w-skeletons. We also explore the global information diffusion efficiency of w-skeletons using simulations, from which we can see that the ties in the high w-skeletons play important roles in information diffusion. Identifying such a simple structure of a w-skeleton is a step forward toward understanding and representing the topological structure of weighted social networks.

A Study of Stock Price Behavior in Taiwan via Residual Income Valuation Theory and Structural Identification

This study adopts the methodology introduced by Lee (2006) to analyze stock prices in response to information shocks in six of Taiwan's stock market sectors and present market anomalies utilizing behavioral finance theory. Using the Residual Income Model (RIM) of equity valuation, we specified our empirical model to identify structural fundamental and nonfundamental shocks from reduced-form tangible and intangible news, and we obtained three major results. First, fundamental shock is primarily induced by tangible news and nonfundamental shock by intangible news, suggesting that tangible-oriented RIM can capture the information content of stock prices. Second, impulse response analyses show that investors generally underreact to fundamental shocks and consistently overreact to nonfundamental shocks in the short-run. This finding is compatible with the overconfidence theory of Daniel et al. (1998) in behavioral finance literature. Third, information diffusion efficiency in a market appears to depend on the value relevance quality of its tangible information. This is based on our finding that when tangible information constitutes a higher share of a market's fundamental shock, its price converges faster to the long-run equilibrium associated with the shock.