Information Propagation Research Articles

A deep reinforcement learning method based on a multiexpert graph neural network for flexible job shop scheduling

When addressing flexible job shop scheduling problems (JSPs) via deep reinforcement learning (DRL), disjunctive graphs are commonly selected as the state observations of the agents. The previously developed methods primarily utilize graph neural networks (GNNs) to extract information from disjunctive graphs. However, as the instance scale increases, agents struggle to handle states with varying distributions, leading to reward confusion. To overcome this issue, inspired by the large-scale’mixture-of-experts (MoE)’ model, we propose a novel module, i.e., a multiexpert GNN (ME-GNN), which integrates several approaches through a gating mechanism. Furthermore, the expert systems within the module facilitate lossless information propagation, providing robust support for solving complex cases. The experimental results demonstrate the effectiveness of our method. On synthetic datasets, our approach reduces the required makespan by 1.19%, and on classic datasets, it achieves a reduction of 1.34%. The multiple experts contained in the ME-GNN module enhance the overall flexibility of the system, effectively shortening the makespan.

Identifying vital spreaders in large-scale networks based on neighbor multilayer contributions

IntroductionIdentifying influential spreaders in complex networks is crucial for understanding information propagation and disease immunity. The spreading ability of a node has been commonly assessed through its neighbor information. However, current methods do not provide specific explanations for the role of neighbors or distinguish their individual contributions to the spread of information.MethodsTo address these limitations, we propose an efficient ranking algorithm that strictly distinguishes the contribution of each neighbor in information spreading. This method combines the count of common neighbors with the K-shell value of each node to produce its ranking. By integrating these two factors, our approach aims to offer a more precise measure of a node's influence within a network.ResultsExtensive experiments were conducted using Kendall’s rank correlation, monotonicity tests, and the Susceptible-Infected-Recovered (SIR) epidemic model on real-world networks. These tests demonstrated the effectiveness of our proposed algorithm in identifying influential spreaders accurately.DiscussionFurthermore, computational complexity analysis indicates that our algorithm consumes less time compared to existing methods, suggesting it can be efficiently applied to large-scale networks.

MSA-Net: A Multi-Scale Information Diffusion Model Awaring User Activity Level

Modeling information diffusion on social networks can be used to guide the prediction and control of information propagation and improve the structure and functionality of social networks. Existing information diffusion prediction methods can predict information diffusion paths and its volume by modeling social network structure and user behavior. However, none of the existing methods take user activity level, which is proved to be critical in modeling the information diffusion process, into account, thus weaken the prediction accuracy. To solve this problem, this paper proposes a Multi-Scale Activity Network (MSA-Net) to capture topological and historical affect features for different scales and to predict the users who will be affected at a specific future timestamp with the help of user activity level. Specifically, we first learn the network representation of three scales or levels: micro-scale, meso-scale, and macro-scale, which refers to the user level, intra-community level, and inter-community level, respectively. Then, we introduce the user activity level for each user by using user degree and average number of tweets per time unit to model the individual differences of users to achieve a more accurate prediction. Extensive experiments based on real-world datasets show that MSA-Net achieves a 6.14% improvement in terms of precision, a 6.74% improvement in terms of recall metrics, a 4.26% improvement in terms of F1-score, a 3.15% improvement in terms of MAP, and a 25.78% improvement in terms of NRMSE over the best existing baseline. The code and data are available at https://github.com/tsinghua-fib-lab/MSA-Net.

Models for information propagation on graphs

Abstract We propose and unify classes of different models for information propagation over graphs. In a first class, propagation is modelled as a wave, which emanates from a set of known nodes at an initial time, to all other unknown nodes at later times with an ordering determined by the arrival time of the information wave front. A second class of models is based on the notion of a travel time along paths between nodes. The time of information propagation from an initial known set of nodes to a node is defined as the minimum of a generalised travel time over subsets of all admissible paths. A final class is given by imposing a local equation of an eikonal form at each unknown node, with boundary conditions at the known nodes. The solution value of the local equation at a node is coupled to those of neighbouring nodes with lower values. We provide precise formulations of the model classes and prove equivalences between them. Finally, we apply the front propagation models on graphs to semi-supervised learning via label propagation and information propagation on trust networks.

Using N2pc variability to probe functionality: Linear mixed modelling of trial EEG and behaviour.

This paper has two concurrent goals. On one hand, we hope it will serve as a simple primer in the use of linear mixed modelling (LMM) for inferential statistical analysis of multimodal data. We describe how LMM can be easily adopted for the identification of trial-wise relationships between disparate measures and provide a brief cookbook for assessing the suitability of LMM in your analyses. On the other hand, this paper is an empirical report, probing how trial-wise variance in the N2pc, and specifically its sub-component the NT, can be predicted by manual reaction time (RT) and stimuli parameters. Extant work has identified a link between N2pc and RT that has been interpreted as evidence of a direct and causative relationship. However, results have left open the less-interesting possibility that the measures covary as a function of motivation or arousal. Using LMM, we demonstrate that the relationship only emerges when the NT is elicited by targets, not distractors, suggesting a discrete and functional relationship. In other analyses, we find that the target-elicited NT is sensitive to variance in distractor identity even when the distractor cannot itself elicit consistently lateralized brain activity. The NT thus appears closely linked to attentional target processing, supporting the propagation of target-related information to response preparation and execution. At the same time, we find that this component is sensitive to distractor interference, which leaves open the possibility that NT reflects brain activity responsible for the suppression of irrelevant distractor information.

Does public sector performance information impact stakeholders? Evidence from a meta‐analysis

AbstractPerformance information (PI) has received significant attention in public administration research. However, evaluating the impact of public sector PI on stakeholders is challenging due to varying empirical results. Drawing on information propagation theory, as well as social and cognitive psychology, we conduct a meta‐analysis to examine the effect of public sector PI. Using 461 effect sizes from 75 studies, the meta‐analysis reveals PI's positive effects on stakeholder attitudes, behaviors, and perceptions of performance. Moreover, the effects tend to be stronger when PI is sent by third parties, received by citizens, delivered with positive valence, presented in absolute forms, and disseminated in law enforcement administrative subfields and in societies characterized by low power distance. The findings reinforce the significance of public sector PI and illuminate the complex interplay between it and stakeholder responses.

Hierarchical graph-based integration network for propaganda detection in textual news articles on social media

During the Covid-19 pandemic, the widespread use of social media platforms has facilitated the dissemination of information, fake news, and propaganda, serving as a vital source of self-reported symptoms related to Covid-19. Existing graph-based models, such as Graph Neural Networks (GNNs), have achieved notable success in Natural Language Processing (NLP). However, utilizing GNN-based models for propaganda detection remains challenging because of the challenges related to mining distinct word interactions and storing nonconsecutive and broad contextual data. In this study, we propose a Hierarchical Graph-based Integration Network (H-GIN) designed for detecting propaganda in text within a defined domain using multilabel classification. H-GIN is extracted to build a bi-layer graph inter-intra-channel, such as Residual-driven Enhancement and Processing (RDEP) and Attention-driven Multichannel feature Fusing (ADMF) with suitable labels at two distinct classification levels. First, RDEP procedures facilitate information interactions between distant nodes. Second, by employing these guidelines, ADMF standardizes the Tri-Channels 3-S (sequence, semantic, and syntactic) layer, enabling effective propaganda detection through related and unrelated information propagation of news representations into a classifier from the existing ProText, Qprop, and PTC datasets, thereby ensuring its availability to the public. The H-GIN model demonstrated exceptional performance, achieving an impressive 82% accuracy and surpassing current leading models. Notably, the model’s capacity to identify previously unseen examples across diverse openness scenarios at 82% accuracy using the ProText dataset was particularly significant.

The Impact of Use Social Media in Communication Flow from State House to Traditional Media in Tanzania

The media has historically obtained and disseminated government information to the public through newspapers, radio, and television. Before the advent of social media, Journalists obtained information from the State House through telegraph, telephone, fax, and in-person encounters between the President's communication officers and journalists. Over twenty years, the State House Communications Department has enhanced its communication methods and effectively utilized social media platforms to engage with journalists and the public. However, little focus on scientific research has been done to assess the impact of social media on communication flow from Tanzania’s state house to traditional media. Therefore, the study evaluated the application of new media in the Presidential Communication Directorate in disseminating news to the mainstream media. The study used a descriptive design and mixed-methods approach, combining qualitative and quantitative methodologies. The qualitative delved into individual viewpoints on news collection using new media, while the quantitative evaluated respondents' perspectives. The State House Communication Unit has shifted its use of social media platforms enhancing networking among journalists, providing quick information access, and being cost-effective. However, the shift has led to decreased personal communication, a decline in exclusive news, an increase in false information propagation, and the emergence of citizen journalism. Journalists should improve their social media skills to maintain high-quality media reporting. The Statehouse should enhance its 24/7 media unit operations. The study highlighted the challenges and significance of using social media for news dissemination and gathering. The study involved media professional practitioner’s and President's communication officers who identified significance and challenges and suggested how to improve media production and contents.

Finding polarized communities and tracking information diffusion on Twitter: a network approach on the Irish Abortion Referendum.

The analysis of social networks enables the understanding of social interactions, polarization of ideas and the spread of information, and therefore plays an important role in society. We use Twitter data-as it is a popular venue for the expression of opinion and dissemination of information-to identify opposing sides of a debate and, importantly, to observe how information spreads between these groups in our current polarized climate. To achieve this, we collected over 688 000 tweets from the Irish Abortion Referendum of 2018 to build a conversation network from users' mentions with sentiment-based homophily. From this network, community detection methods allow us to isolate yes- or no-aligned supporters with high accuracy (90.9%). We supplement this by tracking how information cascades spread via over 31 000 retweet cascades. We found that very little information spread between polarized communities. This provides a valuable methodology for extracting and studying information diffusion on large networks by isolating ideologically polarized groups and exploring the propagation of information within and between these groups.

Electrical synapses in the optic nerve head – A novel model of gap junctions' involvement in optic nerve function

Retinal neurons are part of the central nervous system, making them unable to regenerate spontaneously when damaged. Glaucoma, a progressive optic neuropathy, is believed to be the primary cause of severe vision impairment or permanent vision loss. Connexins, essential channel proteins that create gap junctions between neighboring cells, including neurons, play a significant role in maintaining cell stability. However, in neurons, they can also contribute to the spread of apoptotic insults, which can lead to secondary neurodegeneration.The discovery of previously unknown electrical synapses known as gap junctions between ON axons, which directly connect axons within bundles in the ON head, potentially speeds up signal transmission along the ON and allows for modulation of signal passage from the retina to the brain. By enabling crosswise conduction within ON bundles, these synapses could potentially help bypass localized damage within axons. It is hypothesized that the density and conductivity of these synapses may play a crucial role in determining the susceptibility of the ON to developing various impairments that lead to symptomatic pathologies.Research has shown that temporarily blocking ON electrical synapses chemically can slow down the conduction of visual signals. In cases of structural or functional impairments in axons, signals could potentially be transmitted crosswise through gap junctions to neighboring axons, thereby maintaining the continuity of the syncytial ON structure and preventing the blockage of visual information propagation. This discovery holds significant implications for understanding the development of different optic neuropathies and highlights a potential new target for therapeutic interventions.

Node Sequencing and Visualization Model Construction of Information Propagation Temporal Network based on Interlayer Coupling Intensity Attenuation

Node Sequencing and Visualization Model Construction of Information Propagation Temporal Network based on Interlayer Coupling Intensity Attenuation

Using ERPs to unveil the authenticity evaluation and neural response to online rumors

The rapid propagation of information in the digital epoch has brought a surge of rumors, creating a significant societal challenge. While prior research has primarily focused on the psychological aspects of rumors—such as the beliefs, behaviors, and persistence they evoke—there has been limited exploration of how rumors are processed in the brain. In this study, we experimented to examine both behavioral responses and EEG data during rumor detection. Participants evaluated the credibility of 80 randomly presented rumors, and only 22% were able to identify false rumors more accurately than by random chance. Our ERP findings reveal that truth judgments elicit stronger negative ERP responses (N400) compared to false judgments, while false judgments are associated with larger positive ERP responses (P2, P3, and LPP). Additionally, we identified gender differences in brain activity related to rumor detection, suggesting distinct cognitive strategies. Men demonstrated greater P2 and enhanced N400 responses, while women exhibited larger P3 and LPP amplitudes. This study is among the first to investigate the neural patterns underlying rumors recognition and to highlight gender disparities in decision-making related to rumors.

DANI: fast diffusion aware network inference with preserving topological structure property

Numerous algorithms have been proposed to infer the underlying structure of the social networks via observed information propagation. The previously proposed algorithms concentrate on inferring accurate links and neglect preserving the essential topological properties of the underlying social networks. In this paper, we propose a novel method called DANI to infer the underlying network while preserving its structural properties. DANI is constructed using the Markov transition matrix, which is derived from the analysis of time series cascades and the observation of node-node similarity in cascade behavior from a structural perspective. The presented method has linear time complexity. This means that it increases with the number of nodes, cascades, and the square of the average length of cascades. Moreover, its distributed version in the MapReduce framework is scalable. We applied the proposed approach to both real and synthetic networks. The experimental results indicated DANI exhibits higher accuracy and lower run time compared to well-known network inference methods. Furthermore, DANI preserves essential structural properties such as modular structure, degree distribution, connected components, density, and clustering coefficients. Our source code is available on GitHub (https://github.com/AryanAhadinia/DANI).

Information propagation dynamics on heterogeneous-homogeneous coupling bi-layer networks

The proliferation of multi-platform network information has expanded communication channels for users, enabling the integration and dissemination of information across both Social Networking Services (SNS)-type app and Instant Message (IM)-type app. With the intensification of convergent communication, some users in the two types of apps show active alternation in spreading information to each other’s platforms. The study of the evolution trend of information in different platforms is of great practical significance for the mastery of the communication law. This study synthesizes the following three points: (1) The information in SNS-type app diffuses from key nodes with more followers to ordinary nodes, showing the characteristics of heterogeneous network with radial and explosive propagation. (2) The information in IM-type app mainly depends on the “relationship chain” diffusion, showing the characteristics of homogeneous network with gradual and multi-cluster propagation. (3) SNS-type apps and IM-type apps with some users showing coupled propagation characteristics. Therefore, this study constructs the heterogeneous-homogeneous asymmetric coupling two-layer network information propagation dynamics model. The propagation threshold R0 and the stability of the model are derived theoretically. Real network data sets are used to simulate the platform fusion. Numerical simulations confirm the rationality of the propagation threshold and perform changes analyses of parameters, such as the degree of cross-circulation of platforms, users’ tendency of multi-platform expression, and changes in users’ behaviors towards information dissemination. Simulation results reveal that promoting platform integration can improve communication efficiency in the real world. Dual-platform communication by IM-platform spreaders substantially contributes to the growth in the number of SNS-platform spreaders. The higher the level of disinterest in dual-platform spreaders, the more likely it is to inhibit the growth of spreaders and removers, with IM-type app demonstrating more pronounced effects.

Revisiting Information Cascades in Online Social Networks

It is widely believed that a user’s activity pattern in Online Social Networks (OSNs) is strongly influenced by their friends or the users they follow. Building on this intuition, numerous models have been proposed over the years to predict information propagation in OSNs. Many of these models drew inspiration from the process of infectious spread within a population. While this approach is definitely plausible, it relies on knowledge of users’ social connections, which can be challenging to obtain due to privacy concerns. Moreover, while a significant body of work has focused on predicting macro-level features, such as the total cascade size, relatively little attention has been given to the prediction of micro-level features, such as the activity of an individual user. In this study we aim to address this gap by proposing a method to predict the activity of individual users in an OSN, relying solely on their interactions rather than prior knowledge of their social network. We evaluated our results on four large datasets, each comprising over 14 million tweets, recorded on X social network across four different topics over several month. Our method achieved a mean F1 score of 0.86, with a best result of 0.983.

Where is "Out There"?

Experimental evidence and verified individual accounts show that visual and other types of information from distant minds and environments can enter the conscious as well as the unconscious mind of an individual and can measurably affect brain and body function. The information appears to pass through every physical obstacle and does not degrade in quality with increasing distance in the path between source and receiver. No signal can be detected with current physical instrumentation. What is the medium in which this “psi-encoded information” appears to propagate? What is its format? What is its lifetime? Selected examples of apparent psi information propagation from differing types of sources are presented and analyzed. In order to account for the behavior observed in the examples, a worldview developed in the Advaitic thought tradition is considered, in which a non-physical domain interacts with the physical domain of matter-energy and spacetime. A main currency of the non-physical domain is postulated to be psi-encoded information. It is concluded that this information does not propagate through space. Other characteristics of the non-physical domain and its content are inferred from the examples. They provide a framework in which the nature of psi-encoded information and its processing may be further explored.

THCA: Three-Hop Congestion Awareness Routing Mechanism in WiNoC

Wireless on-chip networks suffer from serious congestion problems due to the expansion of their network size and the joining of wireless nodes, and token passing is very inefficient in the case of low network injection. Aiming at the congestion problem and the inefficiency of token passing in wireless on-chip networks, this paper designs a three-hop congestion-aware routing mechanism in WiNoC. In the scheme of this paper, first, the congestion information is written into Head Flit for propagation through the information piggybacking method, in which the information piggybacking method does not introduce congestion information propagation overhead, second, the port with the smallest congestion value is selected for transmission through the three-hop congestion-aware routing algorithm to reduce the congestion within the subnetwork, and then the wireless node congestion is mitigated through the new wired and wireless packet division method, and finally, the power of wireless nodes is reduced through the dynamic MAC mechanism to reduce the power consumption of wireless nodes. The experimental results show that this paper’s scheme can reduce the intra-subnet congestion, balance the inter-subnet load, reduce the power consumption of wireless nodes, and reduce the latency by 44.6% compared with the basic wireless on-chip network using adaptive routing algorithm, and reduce the latency by 21.8% compared with the wireless on-chip network using global congestion-aware routing algorithm; in different traffic patterns, this paper achieves the highest saturation throughput in different traffic patterns, and the additional hardware overhead of the router increases by only 0.04%.

InfoPro: Locally Supervised Deep Learning by Maximizing Information Propagation

InfoPro: Locally Supervised Deep Learning by Maximizing Information Propagation

Towards adaptive information propagation and aggregation in hypergraph model for node classification

Towards adaptive information propagation and aggregation in hypergraph model for node classification

Precision Adverse Drug Reactions Prediction with Heterogeneous Graph Neural Network.

Accurate prediction of Adverse Drug Reactions (ADRs) at the patient level is essential for ensuring patient safety and optimizing healthcare outcomes. Traditional machine learning-based methods primarily focus on predicting potential ADRs for drugs, but they often fall short of capturing the complexity of individual demographics and the variations in ADRs experienced by different people. In this study, a novel framework called Precise Adverse Drug Reaction (PreciseADR) for patient-level ADR prediction is proposed. The approach effectively integrates relations between patients and ADRs, and harnesses the power of heterogeneous Graph Neural Networks (GNNs) to address the limitations of traditional methods. Specifically, a heterogeneous graph representation of patients is constructed, encompassing nodes that represent patients, diseases, drugs, and ADRs. By leveraging edges in the graph, crucial connections are captured such as a patient being affected by diseases, taking specific drugs, and experiencing ADRs. Next, a GNN-based model is utilized to learn latent representations of the patient nodes and facilitate the propagation of information throughout the graph structure. By employing patient embeddings that consider their diseases and drugs, potential ADRs can be accurately predicted. The PreciseADR is dedicated to effectively capturing both local and global dependencies within the heterogeneous graph, allowing for the identification of subtle patterns and interactions that play a significant role in ADRs. To evaluate the performance of the approach, extensive experiments are conducted on a large-scale real-world healthcare dataset with adverse reports from the FDA Adverse Event Reporting System (FAERS). Experimental results demonstrate that the PreciseADR achieves superior predictive performance in identifying patient-level ADRs, surpassing the strongest baseline by 3.2% in AUC score and by 4.9% in Hit@10.