Large Social Networks Research Articles

Social networks from dailies: the observer’s point of view

In this paper, we explore the hypothesis that the social network described by an observer in a written document provides a discernible cross-section of the underlying social network. Specifically, we propose that the subnetwork described by the observer is extracted from large social network with a Barabási-Albert structure, following two key premises: (1) the observer identifies and reports nodes and interactions in his local environment, and (2) the observer records the most relevant nodes—those with a high number of connections—as well as the most significant portions of their local environment. We test this hypothesis using personal dailies written in New Spain and Mexico between 1776 and 1873. We compare the structure of these dailies networks to one depicted by a typical subnetwork of a Barabási-Albert network, obtained following the two premises mentioned above. To support our findings, we compare them to the social networks described in two well-known novels. In this work, we implicitly assume the hypothesis that there is a complete, non-subjective social network, including our observer, with a Barabási-Albert structure. The evolution of this underlying social network can shed light on the dynamics of a society over time. Our approach could give place to indicators of the changes in the structure of this enveloping network. This is especially important since, although our historical sources do not offer sufficient data to reconstruct the processes in their entirety, the approach could supply clues or fragments from which we could understand the structure and dynamics of societies over time.

Effect of Socialization on Alzheimer's Disease During the COVID-19 Pandemic.

Age is the strongest risk factor for Alzheimer's disease, a neurodegenerative disease where beta-amyloid plaques accumulate in the brain. Elderly individuals, especially those in nursing homes, were burdened by social isolation during the COVID-19 pandemic. The purpose of this literature review is to describe the effectiveness of social engagement and how combating isolation can have a neuroprotective effect on individuals at risk for Alzheimer's disease. We conducted a search in PubMed examining articles from 2010 to 2023 that discussed the impact of socialization on Alzheimer's disease, particularly during the COVID-19 pandemic. Our search terms were "Alzheimer's Disease + Socialization," "Social Isolation + Alzheimer's Disease," "Alzheimer's Disease + COVID-19," "COVID-19 + Social Isolation," and "Social Interventions + Alzheimer's Disease." Inclusion criteria consisted of patients ages 60 and older with Alzheimer's disease, mention of social isolation or engagement, and any relationship between COVID-19 and Alzheimer's disease. Exclusion criteria were defined as other dementias, non-social interventions, and the effects of different viruses on Alzheimer's disease. After the screening process, 30 articles were included, along with six articles that were suitable to the topic. Of the 36 total articles, 19 focused on an intervention involving socialization; eight explored the effect of social isolation during COVID-19 on patients with Alzheimer's disease; five articles examined social isolation as a risk factor for dementia; and four articles discussed the effect of socialization on Alzheimer's disease. A few studies reported that having a large social network can improve cognition and mood for patients with Alzheimer's disease. Studies reported that interventions such as volunteering, video calls, group art classes, animal interactions, and others produced positive outcomes in Alzheimer's patients, but not all were statistically significant. Our review found a consistent association between a socially integrated lifestyle and a decreased incidence of early-onset dementia. Although not all interventions were solely social, a strong social structure remained at the core of a healthy aging process.

Whistlerlib: a distributed computing library for exploratory data analysis on large social network datasets

Whistlerlib: a distributed computing library for exploratory data analysis on large social network datasets

Prediction of influential nodes in social networks based on local communities and users’ reaction information

Identifying influential nodes is one of the basic issues in managing large social networks. Identifying influence nodes in social networks and other networks, including transportation, can be effective in applications such as identifying the sources of spreading rumors, making advertisements more effective, predicting traffic, predicting diseases, etc. Therefore, it will be important to identify these people and nodes in social networks from different aspects. In this article, a new method is presented to identify influential nodes in the social network. The proposed method utilizes the combination of users’ social characteristics and their reaction information to identify influential users. Since the identification of these users in the large social network is a complex process and requires high processing power and time, clustering and identifying communities have been used in the proposed method to reduce the complexity of the problem. In the proposed method, the structure of the social network is divided into its constituent communities and thus the problem of identifying influential nodes (in the entire network) turns into several problems of identifying an influential node (in each community). The suggested method for predicting the nodes first predicts the links that may be created in the future and then identifies the influential nodes based on an iterative strategy. The proposed algorithm uses the criteria of centrality and influence domain to identify this category of users and performs the identification process both at the community and network levels. The efficiency of the method has been evaluated using real databases and the results have been compared with previous works. The results demonstrate that the proposed method provides a more suitable performance in detecting the influential nodes and is superior in terms of accuracy, recall and processing time.

(Un)leashed potentials: an activist-centered perspective on the political mobilization of motorcycle taxi drivers in eastern DRC

ABSTRACT The re-emergence of competitive elections in Africa has sparked new interest in political mobilization on the continent. Much of this literature focusses on political actors and the strategies they use to mobilize. If the campaign activists are reviewed at all, they are often categorized by their ethnic or religious affiliation. Thereby, abilities and potentials for political participation, which are important within conceptual literature on political mobilization, are side-lined. This article uses an activist-centered perspective to review the political mobilization of motorcycle taxi drivers, eastern Congo. Based on empirical research in 2020 and 2021, it evaluates the mobilization potentials of the so-called motards and their deployment during political action. The article finds that motards possess unique potentials for participating in political campaigns due to their mobility and large social networks but only partially deploy them. While their mobility potential is widely utilized, the mobilization of their social networks is hampered by a lack of allegiances and infrequent contacts with politicians. Based on the research findings, the article advocates expanding the perception of political mobilization in Africa by reflecting on the preconditions of mobilizability such as enabling potentials, interactions with activists and aligned political strategies.

Clustering Social Networking Data With K-Means Algorithm Using R Language

The main objectives of this research work are to report detailed empirical studies on sequential and parallel algorithms for diverse clustering tasks executed on very large social network datasets using memory efficient out-of-core approaches. We evaluate the spark implementation for R on Cloudera using the data from social media review datasets like k-means and hierarchical clustering to rank these algorithms. This implementation leverages the YouTube dataset from UCI Machine Learning Repository. Our goal is to compare a few algorithms, so we can know exactly how accurately these models are performing. Ultimately we want to deal with testing and ranking clustering method, and mining and finally clustering massive amounts of unstructured data.

Performance Evaluation of Python Libraries for Community Detection on Large Social Network Graphs

The development of social networking platforms has transformed the way people interact. The increasing number of social media users generates vast amounts of data and creates communities among users that are interesting to analyze. Community detection holds significant benefits in understanding network structures and providing insights into these communities. Community detection can be performed using various libraries and algorithms. However, the abundance of options can be challenging in selecting the appropriate library and algorithm. Therefore, this research evaluates the performance of community detection algorithms on social network graph datasets using several Python libraries such as NetworkX, igraph, Scikit-Network, and CDlib, as well as Louvain and Label Propagation algorithms. The findings of this study reveal that igraph is an ideal library, with fast execution time, efficient memory usage, and user-friendliness. Additionally, the Louvain algorithm is ideal for community detection and exhibits high modularity values.

Exploring Within-Gender Differences in Friendships Using an Online Social Network

People tend to befriend others similar to themselves, generating a pattern called homophily. However, existing studies on friendship patterns often rely on surveys that assess the perspective of relatively few participants on their friendships but do not measure actualized friendship patterns. Here, we used data from a large Slovakian online social network to assess the role of gender, age, and body mass index (BMI) in same-gender online connections among more than 400,000 users. We found that age and BMI homophily occurred in both men’s and women’s same-gender connections, but somewhat more strongly among men’s. Yet, as women diverged in BMI, their connections were less likely to be reciprocated. We discuss how the evolutionary legacy of men’s coalitional competition (e.g., warfare) and women’s mating competition or recruitment of allocare providers might contribute to these patterns in modern same-gender relationships. For example, men’s engagement in physical activities may lead to similar formidability levels among their same-gender peers. Altogether, our findings highlight the importance of trait similarity to same-gender friendship patterns.

NDI-PPO: Node degree increment-based proximal policy optimization method for employment community discovery in online social networks

Purpose: aiming at the limitations of pre-input parameters in the complex network overlapping community discovery algorithm based on tag propagation in real networks and the problems of tag redundancy, method: a node degree increment-based proximal policy optimization method for community discovery in online social networks is proposed (named NDI-PPO). Process: by applying the cohesion idea and introducing the concept of modularity increment, a social network great community is constructed from the bottom up according to the criteria of community division. For the problem that the number of iterative steps is sensitive to the strategy gradient algorithm, we adopt an improved PPO to improve the efficiency of feature extraction. In label updating, the maximum clique is used as the core unit to update the labels and weights of the maximum maximum clique adjacent nodes from the center to the periphery using intimacy, and the weights of the non-maximum maximum clique adjacent nodes are updated by means of the maximum weight. In the post-processing stage, the adaptive threshold method is used to remove the noise in the node label, which effectively overcomes the limitation of the number of pre-input overlapping communities in the real network. Result: The simulation results show that the proposed community discovery algorithm NDI-PPO is superior to other advanced algorithms, the time complexity is greatly reduced, and it is suitable for community discovery in large social networks.

The contagious spread of bullying among Chinese adolescents through large school-based social networks

This study investigated the spread of bullying behaviors in school-based social networks among 13,195 Chinese adolescents (48.7% female). Using a modified Chinese version of the Multidimensional Peer Victimization Scale, we examined five types of bullying. The study focused on the association between an individual’s bullying behavior and that reported by their friends, the dose-response correlation between the number of friends involved in bullying and the prevalence of bullying behavior, and the moderators influencing the magnitude of peer influence. Our results indicate a significant contagion effect in bullying behaviors. A participant was more likely to engage in all five types of bullying if their friends reported similar behaviors. This influence persisted up to three degrees of separation for physical, verbal, possession, and sexual bullying, and extended to four degrees for relational bullying. A strong dose-response relationship was observed, indicating that the more friends involved in bullying, the higher the likelihood of a participant engaging in similar behaviors. Gender dynamics played a crucial role; females were more influenced by same-gender peers in relational and verbal bullying, whereas males showed a higher influence from male peers in physical and sexual bullying. These findings highlight the complex nature of bullying spread within social networks and the importance of considering peer influence and gender dynamics. They carry significant implications for future research, school policies, and public health interventions targeting bullying prevention.

Emergence of keystone individuals in the social networks of the ant Camponotus fellah

Community ecology and sociobiology share a number of problems (e.g. understanding part-to-whole relationships), so sharing methods may be beneficial. In this paper, we re-analyze a large social network database for Camponotus fellah ants, from the perspective of keystone individuals, based on methods developed in community ecology. We study different network types for six colonies, over a time series of 10 days. These include the total network of weighted interactions, the subnetworks for the castes and the subnetworks for Queen-linked and Queen-independent individuals. We perform time-independent (i.e. average values) and temporal (i.e. trends) analyses. The most commonly used global network metrics showed high variability in time but no clear trends. Yet, the variability of a network centrality index (topological importance, TI), developed earlier in community ecology, shows clear increase in each colony over time, suggesting the emergence of keystone individuals as a general tendency in all studied ant colonies.

Finding Rumor Source in large Social Network

Finding Rumor Source in large Social Network

Neural attentive influence maximization model in social networks via reverse influence sampling on historical behavior sequences

Influence maximization in social networks aims to identify users with the highest influence in the network and leverage them as initial spreaders to maximize the revenue of influence. Recently, an increasing number of studies have drawn attention to the study of influence maximization in multi-entity social networks, achieving some degrees of success. However, most of them still have two major shortcomings. First, most of the previous work oversimplifies the correlation between the target entity and other entities, and overlooks the influence of user’s interest preference on information propagation. Second, the majority of existing methods face a trade-off between operational efficiency and solution accuracy, making it difficult to be effectively applied to large social networks. To fill this gap, this paper proposes a Deep neural network model based on Historical Behavior Sequences, named DHBS. DHBS consists of two core components: a Deep neural network model Incorporating Multi-head Self-attention, named DIMS; and a Reverse Influence Sampling algorithm based on Historical Behavior Sequence, named RIS-HBS. Specifically, DIMS captures the dynamic interest changes of users through its attention mechanism, enabling more accurate modeling of entity correlation in multi-entity social networks. RIS-HBS efficiently identifies a set of high-influential users for the target entity by incorporating the concept of reverse influence sampling. Experimental results on four publicly available network datasets demonstrate that DHBS outperforms baseline methods in terms of prediction accuracy, expected spread, and scalability, and DIMS plays a key role on RIS-HBS in the DHBS model.

Social Alignment Contagion in Online Social Networks

Researchers have already observed social contagion effects in both in-person and online interactions. However, such studies have primarily focused on users’ beliefs, mental states, and interests. In this article, we expand the state of the art by exploring the impact of social contagion on social alignment, i.e., whether the decision to socially align oneself with the general opinion of the users on the social network is contagious to one’s connections on the network or not. The novelty of our work in this article includes: 1) unlike earlier work, this article is among the first to explore the contagiousness of the concept of social alignment on social networks; 2) our work adopts an instrumental variable approach to determine reliable causal relations between observed social contagion effects on the social network; and 3) our work expands beyond the mere presence of contagion in social alignment and also explores the role of population heterogeneity on social alignment contagion. Based on the systematic collection and analysis of data from two large social network platforms, namely, Twitter and Foursquare, we find that a user’s decision to socially align or distance from social topics and sentiments influences the social alignment decisions of their connections on the social network. We further find that such social alignment decisions are significantly impacted by population heterogeneity.

Multidimensional social connectedness of sexual and gender minority midlife and older adults: Findings from the National Health, Aging, and Sexuality/Gender Study (NHAS).

This article introduces the multidimensional properties of social connectedness among sexual and gender minority (SGM) midlife and older adults and examines the relationship between these properties and general health. Data were analyzed from Aging With Pride: National, Aging, and Sexuality/Gender Study, including 2,450 SGM adults aged 50 and older in the United States. The structure, function, and quality of interpersonal relations as well as community-level activities and engagement were measured through a self-administered survey and an in-person interview. Findings indicated that SGM midlife and older adults, on average, had a large social network with high bridging potential and low density, consisting of more nonrelative family members than immediate family members. They also showed frequent availability of social support, moderate or higher satisfaction with interpersonal relations, and moderate SGM community engagement. Properties of social connectedness differed by gender, sexual identity, and gender identity, with SGM men, sexually diverse women, and transgender people showing distinct challenges in interpersonal relations. All aspects of social connectedness were positively associated with good general health, particularly network diversity, outdoor leisure activity engagement, and access to health-related decision support, controlling for age and chronic conditions. Care receiving and loneliness were negatively associated with good general health. Intervention development can target these factors to promote social and community connectivity and reduce the negative health effects of persistent social stressors. This study underscores the necessity of addressing all facets (i.e., structure, function, and quality) of interpersonal relations encompassing both immediate and chosen family as well as community-level social connectedness. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Multimodal lifestyle engagement patterns support cognitive stability beyond neuropathological burden

BackgroundModifiable lifestyle behaviors account for a large proportion of dementia risk. However, the combined contributions of multidomain lifestyle patterns to cognitive aging are poorly understood, as most studies have examined individual lifestyle behaviors in isolation and without neuropathological characterization. This study examined data-driven patterns of lifestyle behaviors across multiple domains among older adults and tested their associations with disease-specific neuropathological burden and cognitive decline.MethodsParticipants included 2059 older adults enrolled in the longitudinal Memory and Aging Project (MAP) at the Rush Alzheimer’s Disease Center; none of whom had dementia at baseline (73% no cognitive impairment (NCI), 27% mild cognitive impairment [MCI]). All participants completed cognitive testing annually. Lifestyle factors were measured during at least one visit and included (1) actigraphy-measured physical activity, as well as self-reported (2) sleep quality, (3) life space, (4) cognitive activities, (5) social activities, and (6) social network. A subset of participants (n = 791) had autopsy data for which burden of Alzheimer’s disease (AD), cerebrovascular disease (CVD), Lewy body disease, and hippocampal sclerosis/TDP-43 was measured. Latent profile analysis across all 2059 participants identified distinct subgroups (i.e., classes) of lifestyle patterns. Linear mixed-effects models examined relationships between lifestyle classes and global cognitive trajectories, with and without covarying for all neuropathologies. Classes were also compared on rates of incident MCI/dementia.ResultsFive classes were identified: Class 1Low Life Space (lowest lifestyle engagement), Class 2PA (high physical activity), Class 3Low Avg (low to average lifestyle engagement), Class 4Balanced (high average lifestyle engagement), and Class 5Social (large social network). Classes 4Balanced and 5Social had the lowest AD burden, and Class 2PA had the lowest CVD burden. Classes 2–5 had significantly less steep global cognitive decline compared to Class 1Low Life Space, with comparable effect sizes before and after covarying for neuropathological burden. Classes 4Balanced and 5Social exhibited the lowest rates of incident MCI/dementia.ConclusionsLifestyle behavior patterns among older adults account for differential rates of cognitive decline and clinical progression. Those with at least average engagement across all lifestyle domains exhibit greater cognitive stability after adjustment for neuropathology, highlighting the importance of engagement in multiple healthy lifestyle behaviors for later life cognitive health.

Generalized morality culturally evolves as an adaptive heuristic in large social networks.

Why do people assume that a generous person should also be honest? Why do we even use words like "moral" and "immoral"? We explore these questions with a new model of how people perceive moral character. We propose that people vary in the extent to which they perceive moral character as "localized" (varying along many contextually embedded dimensions) versus "generalized" (varying along a single dimension from morally bad to morally good). This variation might be partly the product of cultural evolutionary adaptations to different kinds of social networks. As networks grow larger, perceptions of generalized morality are increasingly valuable for predicting cooperation during partner selection, especially in novel contexts. Our studies show that social network size correlates with perceptions of generalized morality in United States and international samples (Study 1) and that East African hunter-gatherers with greater exposure outside their local region perceive morality as more generalized compared to those who have remained in their local region (Study 2). We support the adaptive value of generalized morality in large and unfamiliar social networks with an agent-based model (Study 3), and in experiments where we manipulate partner unfamiliarity (Study 4). Our final study shows that perceptions of morality have become more generalized over the last 200 years of English-language history, which suggests that it may be coevolving with rising social complexity and anonymity in the English-speaking world (Study 5). We discuss the implications of this theory for the cultural evolution of political systems, religion, and taxonomical theories of morality. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Social media in the Global South: A Network Dataset of the Malian Twittersphere

With the expansion of mobile communications infrastructure, social media usage in the Global South is surging. Compared to the Global North, populations of the Global South have had less prior experience with social media from stationary computers and wired Internet. Many countries are experiencing violent conflicts that have a profound effect on their societies. As a result, social networks develop under different conditions than elsewhere, and our goal is to provide data for studying this phenomenon. In this dataset paper, we present a data collection of a national Twittersphere in a West African country of conflict. While not the largest social network in terms of users, Twitter is an important platform where people engage in public discussion. The focus is on Mali, a country beset by conflict since 2012 that has recently had a relatively precarious media ecology. The dataset consists of tweets and Twitter users in Mali and was collected in June 2022, when the Malian conflict became more violent internally both towards external and international actors. In a preliminary analysis, we assume that the conflictual context influences how people access social media and, therefore, the shape of the Twittersphere and its characteristics. The aim of this paper is to primarily invite researchers from various disciplines including complex networks and social sciences scholars to explore the data at hand further. We collected the dataset using a scraping strategy of the follower network and the identification of characteristics of a Malian Twitter user. The given snapshot of the Malian Twitter follower network contains around seven million accounts, of which 56,000 are clearly identifiable as Malian. In addition, we present the tweets. The dataset is available at: https://osf.io/mj2qt/

Simultaneous estimation and group identification for network vector autoregressive model with heterogeneous nodes

Individuals or companies in a large social or financial network often display rather heterogeneous behaviors for various reasons. In this work, we propose a network vector autoregressive model with a latent group structure to model heterogeneous dynamic patterns observed from network nodes, for which group-wise network effects and time-invariant fixed-effects can be naturally incorporated. In our framework, the model parameters and network node memberships can be simultaneously estimated by minimizing a least-squares type objective function. In particular, our theoretical investigation allows the number of latent groups G to be over-specified when achieving the estimation consistency of the model parameters and group memberships, which significantly improves the robustness of the proposed approach. When G is correctly specified, valid statistical inference can be made for model parameters based on the asymptotic normality of the estimators. A data-driven criterion is developed to consistently identify the true group number for practical use. Extensive simulation studies and two real data examples are used to demonstrate the effectiveness of the proposed methodology.

A Hybrid Probabilistic Privacy Preserving Based Community Detection Model on Online Social Networking Data

Privacy preserving plays a vital role on the online social networking sites due to high dimensionality and data size. Community detection is used to find the social relationships among the node edges and links. However, most of the conventional models are difficult to process the community structure detection due to high computational time and memory. Also, these models require contextual weighted nodes information for privacy preserving process. In order to overcome these issues, an advanced probabilistic weighted based community detection and privacy preserving framework is developed on the large social networking data. In this model, a filter based probabilistic model is developed to remove the sparse values and to find the weighted community detection nodes and its profiles for privacy preserving process. Experimental results show that the filter based probabilistic community detection framework has better efficiency in terms of normalized mutual information, Q, rand index and runtime (ms).