Modeling user check-ins in social networks to predict future behavior.
problem Predicting future user movements and influence in location-based social networks.
method Probabilistic model based on doubly stochastic point process with periodic decaying kernel for time and time-varying multinomial distribution for location.
result The proposed model outperforms other alternatives in predicting user check-ins.
The paper models crime risk using Foursquare check-ins and mobility data.
problem Understanding and predicting crime risk in urban areas.
method Directed graph of aggregated movement data, region risk factor derivation, DIFFER features.
result Reliable correlations between DIFFER features and crime count observed.
New method reconstructs networks from spatiotemporal data.
problem Network reconstruction from spatiotemporal data.
method Multivariate Hawkes processes using both temporal and spatial information.
result Spatiotemporal approach yields improved network reconstruction.
Study analyzes WiFi check-ins to predict student activities.
problem Limited understanding of daily routines in POI prediction.
method Heterogeneous graph-based method to encode correlations.
result Improved POI prediction on education check-in data.
DeepPos uses deep learning to improve indoor location accuracy.
problem Indoor GPS accuracy issues with mobile devices.
method Supervised autoencoder network for modeling CSI-based environments.
result Robust and efficient indoor localization system developed.
DMPP predicts events in cities using rich contextual data.
problem Predicting events in cities with rich contextual factors.
method Deep Mixture Point Processes model with mixture of kernels and deep neural network for context.
result DMPP outperforms existing methods in event prediction.
Map matching of the GPS trajectory serves the purpose of recovering the original route on a road network from a sequence of noisy GPS observations. It is a fundamental technique to many Location Based Services. However, map matching of a low sampling rate on urban road network is still a challenging task. In this paper…
Deep learning models improve spatio-temporal data mining.
problem Mining valuable knowledge from spatio-temporal data.
method Application of deep learning techniques (CNN, RNN) in various spatio-temporal data mining tasks.
result Deep learning models enhance performance in spatio-temporal data mining.
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++.
Transfer learning helps predict social roles in unlabelled networks.
problem Recognizing social roles in unlabelled social networks.
method Transfer learning with feature transformations.
result Improved role classification accuracy on real-world datasets.
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.
This paper categorizes and analyzes mobile big data from wireless networks.
problem Understanding social characteristics of mobile big data.
method Categorization and analysis of real wireless cellular network data.
result Highlight several research directions in social computing for mobile big data.
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.
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…
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.
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.
Introduces 'social bow tie' to quantify tie strength in social networks.
problem Understanding tie strength and its influencing factors in social networks.
method Introduced 'social bow tie' framework, defined metrics, used random forests and regression models.
result Bow tie metrics are highly predictive of tie strength, and tie strength is influenced by overlapping and non-overlapping social circles.
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.
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.
Study online and offline social interactions using game theory.
problem Effects of online social networking on offline interactions and collective well-being.
method Evolutionary game theory approach to model socialization strategies.
result Self-protective behaviors can lead to non-socially optimal outcomes.
The study improves GCN performance on friendship-based social networks by simulating and augmenting datasets.
problem GCNs' limitations in friendship-based social networks, especially dependency between node neighbourhood order and layer.
method Developed a Python library to simulate social networks with ground truth labels and features. Introduced sDNA as latent variables for nodes.
result Four new GCN variants significantly outperform the original model on 27 out of 30 simulated datasets.
Improved node classification in signed social networks using diffuse interface methods.
problem Classifying nodes in signed social networks (positive and negative interactions).
method Diffuse interface methods based on Ginzburg-Landau functional and extended graph Laplacian.
result Performance improvement in real signed social networks, outperforming state of the art.
New metric improves social message classification on Twitter.
problem Efficient classification of short social messages on online networks.
method Dynamic Time Warping distance for message spreading classification.
result Good classification accuracies on Twitter data.
Model predicts future stock market structure using social and financial network data.
problem Predicting future stock market structure with high accuracy.
method Combines financial and social media network information using a multiplex network approach.
result Up to 40% out-of-sample performance improvement in predicting future market structure.
Paper proposes a probabilistic map-matching method using particle filters.
problem Improving GPS data accuracy by aligning with road network.
method Sequential Monte Carlo algorithm (particle filters).
result Validated technique on GPS data of varying quality.
TTERGM models improve social network predictions by incorporating triadic relationships.
problem Lack of models capturing triadic relationships and social learning theories in temporal network data.
method Introduced TTERGM, a generative model that includes triadic relationships and social learning theory as additional probability distributions. Parameters are estimated via Monte Carlo maximum likelihood.
result TTERGM achieves improved accuracy and fidelity compared to existing models on social network data.
This study examines how topic analysis improves community detection in rating-based social networks.
problem Finding meaningful communities in rating-based social networks.
method The study considers both the contents and topological structures of social networks to find more meaningful communities.
result Topic analysis enhances the identification of meaningful communities in rating-based social networks.
Friend recommendation system using heterogeneous edge embeddings.
problem Inadequate performance of existing network embedding techniques on multi-graph social networks.
method Proposes a method to mine network representation exploiting heterogeneity in multi-graphs.
result Outperforms state-of-the-art baselines on Hike's social network in terms of accuracy and user satisfaction.
Paper detects anomalous edges in social networks using edge exchangeability.
problem Detecting anomalous edges in directed social networks.
method Exploits edge exchangeability and uses conformal prediction theory.
result Proposed anomaly detector has a guaranteed upper bound for false positives.
Algorithm ensures fair information spread in social networks with community structure.
problem Disparities in information coverage between communities in social networks.
method Fits a model to the social network, uses community structure, and determines optimal seed allocations for fair coverage.
result Empirical accuracy demonstrated on simulated and real networks.
An unsupervised neural network learns event truths from social network data.
problem Estimating event truths from conflicting opinions in social networks.
method Autoencoder learns relationships, Bayesian network models agent reliability and social relationships, variational inference estimates hidden variables and parameters.
result The approach outperforms state-of-the-art methods on real datasets.
We demonstrate that a number of sociology models for social network dynamics can be viewed as continuous time Bayesian networks (CTBNs). A sampling-based approximate inference method for CTBNs can be used as the basis of an expectation-maximization procedure that achieves better accuracy in estimating the parameters of…
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 infers multiplex network structure from social interactions.
problem Challenges in understanding diffusion in social networks due to hidden structure and multiple interaction patterns.
method Proposes Multiplex Diffusion Model (MDM) using multivariate marked Hawkes process and topic model.
result MDM more effectively uncovers multiplex network structure compared to previous methods.
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.
Study predicts social relationships using triadic influence from social networks.
problem Difficulty in quantifying social relationships and their dynamics.
method Real social networks of 13 schools, neural networks, high-dimensional embedding.
result Triadic influence achieves highest accuracy in predicting student relationships.
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.
Modeling social network activity through user and topic interaction.
problem Understanding complex dynamics of opinion formation in social networks.
method Mixture of Interacting Cascades (MIC) model of marked multidimensional Hawkes processes.
result MIC achieves superior performance in modeling information cascade spread.
MGpi model predicts social actions in group interactions.
problem Social interaction among multiple agents and groups.
method Deep neural network with Kinesic-Proxemic-Message gate for social signal gating.
result Achieves state-of-the-art performance in group identification.
Economics examines social networks through externality effects.
problem Understanding how individual actions impact others in social networks.
method Analyzes network formation and interactions within networks from an economic perspective.
result Externalities are crucial in explaining network dynamics and behaviors.
When banks choose similar investment strategies the financial system becomes vulnerable to common shocks. We model a simple financial system in which banks decide about their investment strategy based on a private belief about the state of the world and a social belief formed from observing the actions of peers. Observ…
DMAN embeds social images with deep multimodal attention networks.
problem Sub-optimal social image representation for social media data.
method Deep Multimodal Attention Networks (DMAN) that jointly embed multimodal contents and link information.
result DMAN achieves significant improvement in multi-label classification and cross-modal search compared to state-of-the-art image embeddings.
We study a networked version of the minority game in which agents can choose to follow the choices made by a neighbouring agent in a social network. We show that for a wide variety of networks a leadership structure always emerges, with most agents following the choice made by a few agents. We find a suitable parameter…
SLIM model predicts social network polarization using signed links.
problem Polarization and filter bubbles in social networks.
method Signed relational Latent dIstance Model (SLIM) using Skellam distribution.
result SLIM model accurately predicts friendships and animosity in social networks.
The paper develops a method to estimate trust weights in social networks using active sensing.
problem Estimating the relative trust agents place on each other in social networks.
method Regression model based on the steady state equation of the linear DeGroot model, using stubborn agents as influencers.
result The network structure can be revealed when a sufficient number of stubborn agents influence ordinary agents.
A new framework for causal inference in networked settings.
problem Causal inference under network interference.
method Characterize agent network configuration and use it to estimate treatment effects.
result Finite-sample bounds and asymptotically valid tests for policy irrelevance.
Model detects cyberbullying by analyzing participant-vocabulary consistency.
problem Identifying cyberbullying on social media platforms.
method Formulated an objective function based on participant-vocabulary consistency to detect cyberbullying.
result The model can detect new bullying vocabulary, victims, and bullies.
The study combines social interaction data into a single network, identifying stable groups of chimpanzees.
problem Identifying stable groups of chimpanzees based on social interactions over time.
method Network representation, weighted proximity weights, principled loss function, statistical tests.
result The approach detects stable groups of chimpanzees that stay related for a significant length of time.