Study finds Bitcoin crypto currency rate correlates with social network activity.
problem Detecting correlation between crypto currency rate and social network activity.
method Comparative correlation and fractal analysis of time series data.
result Time series of Bitcoin rate and social network activities exhibit self-similar and multifractal properties.
Survey on deep learning for social network analysis.
problem Encoding social network data into useful low-dimensional representations.
method Review of neural network models for node and subgraph embeddings in various network types.
result Advancements in deep learning for complex network analysis.
Paper detects social media influencers affecting financial markets.
problem Impact of social media influencers on financial markets.
method Developed an early warning system for detecting suspicious social network activity.
result Discrepancy in meme and non-meme stocks' reactions to social networks.
Combines global and local features for better social circle prediction in ego-networks.
problem Efficiently analyzing ego-networks with hidden local structures.
method Evolved deep learning techniques to capture both global and local network features.
result Social circle prediction benefits from a combination of global and local features.
Finding meaningful communities in social network has attracted the attentions of many researchers. The community structure of complex networks reveals both their organization and hidden relations among their constituents. Most of the researches in the field of community detection mainly focus on the topological structu…
Analysis of opinion dynamics in social networks plays an important role in today's life. For applications such as predicting users' political preference, it is particularly important to be able to analyze the dynamics of competing opinions. While observing the evolution of polar opinions of a social network's users ove…
Predicting event attendance using social influence from social networks.
problem Predicting people's participation in real-world events.
method Modeling social influence, using non-geotagged posts and social group structures, applying graph embedding techniques, and training a neural network.
result The proposed classifier achieves 89% accuracy on the VFestival dataset, outperforming state-of-the-art methods.
Network analysis helps prevent money laundering by identifying risky clients and suspicious clusters.
problem Preventing money laundering using social network analysis.
method Real-world data analysis, network metrics, predictive models, visual analysis.
result Risk profiles can be predicted using social network metrics.
LoCEC classifies user relationships in large social networks, addressing sparsity issues.
problem Sparse relationship feature and label data in real social platforms.
method Local Community-based Edge Classification (LoCEC) framework with three-phase processing.
result Effective and efficient classification of user relationships in large-scale networks.
Study disrupts Sicilian Mafia networks using data analysis.
problem Challenges in disrupting resilient criminal networks.
method Social Network Analysis methods applied to real-world datasets.
result Neutralizing only 5% of affiliates can disrupt network connectivity by 70%.
Proposes a semi-supervised approach to predict user-level sentiments in social media.
problem Detect and analyze sentiment in social media, especially user-level sentiments.
method Semi-supervised approach using a heterogeneous graph built from social networks, incorporating user influences and multiple types of links.
result Predicts user-level sentiments for specific topics more effectively than previous supervised learning approaches.
Model combines CNN and LSTM for improved sentiment analysis.
problem Improving sentiment analysis accuracy on social media data.
method Ensemble of CNN and Bi-LSTM models for temporal and local structure.
result Ensemble model outperforms individual models and previous works.
New algorithm for federated learning without a central server.
problem Federated learning in unidirectional trust social networks.
method Central Server Free Federated Learning (OPS) method.
result Users can benefit from communication with trusted users in federated learning.
Signed networks contain both positive and negative kinds of interactions like friendship and enmity. The task of node classification in non-signed graphs has proven to be beneficial in many real world applications, yet extensions to signed networks remain largely unexplored. In this paper we introduce the first analysi…
Study fairness in vaccine allocation in social networks.
problem Fairness implications of vaccine allocation strategies in social networks.
method Defined precision disease control problem, used ML Fairness Gym to simulate and analyze.
result Different treatment strategies distribute disease burden differently across subgroups.
Model financial markets with social media influences using hierarchical networks.
problem Understanding social media's impact on financial markets.
method Agent-based model with hierarchical influence network.
result Model accurately simulates real-world financial market behaviors.
Study finds Aave token network has core-periphery structure, with high decentralization predicting better returns.
problem Understanding the actual decentralization in DeFi token transactions on the Ethereum blockchain.
method Applied social network analysis to measure decentralization in Aave token transactions.
result A more decentralized Aave token network predicts higher returns and lower volatility.
The paper models social networks with varying levels of reciprocity.
problem Understanding diverse reciprocal behavior in social networks.
method Developed a preferential attachment model with heterogeneous reciprocity.
result Captures the heavy-tailed nature of empirical degree distributions and identifies multiple user groups.
Large graphs are natural mathematical models for describing the structure of the data in a wide variety of fields, such as web mining, social networks, information retrieval, biological networks, etc. For all these applications, automatic tools are required to get a synthetic view of the graph and to reach a good under…
MONSTOR estimates influence in unseen networks with high accuracy.
problem Estimating and maximizing influence in social networks.
method Inductive machine learning approach replacing Monte Carlo simulations.
result Highly accurate estimates with strong correlation to actual influence.
Paper benchmarks Bengali language classification tasks using MConv-LSTM network.
problem Lack of computational resources for NLP tasks in under-resourced languages like Bengali.
method Built three datasets, BengFastText word embeddings, and MConv-LSTM network for hate speech detection, document classification, and sentiment analysis.
result BengFastText yields up to 92.30%, 82.25%, and 90.45% F1-scores in document classification, sentiment analysis, and hate speech detection respectively.
Visualizes board connections for socially responsible investing insights.
problem Understanding corporate governance and sustainability through board connections.
method Data Visualization tool to reveal connections between Directors and Executives.
result Strength of tool in investigating corporate governance and sustainability.
Fast algorithm for analyzing huge social networks.
problem Analyzing dynamic social networks with large numbers of actors.
method Hierarchical strategy for latent space inference with spline processes and machine learning optimization.
result Can fit millions of nodes in a few minutes.
With the rise of social media, people can now form relationships and communities easily regardless of location, race, ethnicity, or gender. However, the power of social media simultaneously enables harmful online behavior such as harassment and bullying. Cyberbullying is a serious social problem, making it an important…
This paper develops an active sensing method to estimate the relative weight (or trust) agents place on their neighbors' information in a social network. The model used for the regression is based on the steady state equation in the linear DeGroot model under the influence of stubborn agents, i.e., agents whose opinion…
New model addresses instability in hierarchical clustering of social networks.
problem Instability in existing hierarchical clustering algorithms for social networks.
method Introduce T-Stochastic Graphs, a probabilistic model that relaxes ultrametric assumptions. result Prove spectral approach combining Neighbor-Joining is statistically consistent.
This work proposes multiple node representations for graphs, improving link prediction and community analysis.
problem Can nodes be best described by a single vector representation?
method A principled decomposition of the ego-network to learn multiple node representations.
result Improved link prediction accuracy by up to 90% and effective community analysis.
Study uses social network data to analyze regional inflation trends.
problem Analyzing inflation trends using social media data.
method BERT neural networks for identifying pro-inflationary and disinflationary keywords.
result Models can visualize and classify inflationary keywords in different contexts.
Online social networks offer a new way to investigate financial markets' dynamics by enabling the large-scale analysis of investors' collective behavior. We provide empirical evidence that suggests social media and stock markets have a nonlinear causal relationship. We take advantage of an extensive data set composed o…
Model detects insurance fraud using social network analysis.
problem Fraudulent insurance claims by exaggeration or intentional damage.
method Network construction linking claims and parties, BiRank algorithm for fraud score computation, feature extraction from network and claims, supervised model building.
result Network features improve fraud detection performance.
A new method selects features for ERGMs to improve network modeling.
problem ERGM degeneracy creates unrealistic network structures.
method Stochastic step-wise feature selection to overcome computational burden and improve network accommodation.
result Enhanced ERGM modeling with more accurate interpretations.
Improved neural model for social recommendation by integrating social and interest networks.
problem Data sparsity and lack of higher-order relationships in social recommendation.
method DiffNet++ models neural influence diffusion and interest diffusion in a unified framework using a multi-level attention network.
result Extensive experiments on real-world datasets show the effectiveness of DiffNet++.
We discuss social network analysis from the perspective of economics. We organize the presentaion around the theme of externalities: the effects that one's behavior has on others' well-being. Externalities underlie the interdependencies that make networks interesting. We discuss network formation, as well as interactio…
Influential node detection is a central research topic in social network analysis. Many existing methods rely on the assumption that the network structure is completely known \textit{a priori}. However, in many applications, network structure is unavailable to explain the underlying information diffusion phenomenon. To…
The paper analyzes user activities in OSNs using a vector space model.
problem Understanding user interactions and activity patterns in OSNs.
method TF-IDF scheme of Vector Space Model to analyze object-viewer relationships.
result Identified activity relationships among users and objects in OSNs.
We consider a collaborative online learning paradigm, wherein a group of agents connected through a social network are engaged in playing a stochastic multi-armed bandit game. Each time an agent takes an action, the corresponding reward is instantaneously observed by the agent, as well as its neighbours in the social n…
Study analyzes Echo State Network parameters for Rossler attractor dynamics.
problem Understanding the influence of network type on Echo State Network performance.
method Experimental analysis of Echo State Network parameters using Rossler attractor.
result Exploration of how network type affects Echo State Network performance.
Model predicts depression and anxiety from social network dynamics.
problem Predicting mental health from social interactions.
method Developed a predictive model using dynamic social network data.
result Dynamic social network model outperforms static models.
The Efficient Market Hypothesis (EMH) is widely accepted to hold true under certain assumptions. One of its implications is that the prediction of stock prices at least in the short run cannot outperform the random walk model. Yet, recently many studies stressing the psychological and social dimension of financial beha…
SINN combines social science and deep learning for predicting opinion dynamics.
problem Predicting opinion dynamics in social networks using traditional models requires extensive calibration with real data.
method SINN integrates theoretical models and social media data using physics-informed neural networks (PINNs) and matrix factorization.
result SINN outperforms six baseline methods in predicting opinion dynamics on real-world and synthetic datasets.
New methods solve graph sparsity optimization problems faster.
problem Complex graph sparsity optimization problems in disease outbreak monitoring and social network analysis.
method Stochastic variance-reduced gradient-based methods GraphSVRG-IHT and GraphSCSG-IHT.
result Our methods achieve linear convergence speed.
Actors in realistic social networks play not one but a number of diverse roles depending on whom they interact with, and a large number of such role-specific interactions collectively determine social communities and their organizations. Methods for analyzing social networks should capture these multi-faceted role-spec…
AN2VEC disentangles feature and structural information in social networks.
problem Difficulty in separating feature and structural information in social networks.
method Graph Convolutional Networks (GCN) Variational Autoencoder for disentangled node embeddings.
result AN2VEC captures joint information of structure and features better than unshared information.
Paper proposes PP-GCN for fine-grained social event categorization.
problem Challenges in mining social events due to heterogeneous event elements and social network structures.
method Design an event meta-schema, build an HIN, propose PP-GCN, and use KIES.
result PP-GCN outperforms other techniques in social event detection and clustering.
Model predicts telecom customer churn with high accuracy.
problem Predicting customers at risk of leaving telecom companies.
method Machine learning and social network analysis on big data platform.
result Model achieved 93.3% AUC, significantly improving churn prediction.
New framework analyzes belief evolution in social networks.
problem Analyzing belief evolution in social networks.
method Proposes a new theoretical framework using Markov chain theory for horizontal and vertical transmission.
result Homophily-based networks do not converge to a single set of beliefs.
Mobile big data contains vast statistical features in various dimensions, including spatial, temporal, and the underlying social domain. Understanding and exploiting the features of mobile data from a social network perspective will be extremely beneficial to wireless networks, from planning, operation, and maintenance…
In online social networks people often express attitudes towards others, which forms massive sentiment links among users. Predicting the sign of sentiment links is a fundamental task in many areas such as personal advertising and public opinion analysis. Previous works mainly focus on textual sentiment classification, …