Study reveals linguistic signs of betrayal in online strategy games.
problem Predicting the dissolution of friendships in online games.
method Characterized dyadic interactions, analyzed temporal patterns, and examined conversational attributes.
result Subtle linguistic cues indicate impending betrayal in online strategy games.
Semi-supervised graph embedding predicts dynamic network links.
problem Predicting links in evolving networks.
method Discrete time semi-supervised approach with weighted loss function combining past and current network information.
result Model outperforms state-of-the-art methods in link dissolution prediction.
New framework for knots on Seifert surfaces, no universal host.
problem Understanding how knots appear on minimal genus Seifert surfaces.
method Directed relation and friendship defined on knot types.
result No single knot is a universal host, but families can be.
In this paper, we explore salient questions about user interests, conversations and friendships in the Facebook social network, using a novel latent space model that integrates several data types. A key challenge of studying Facebook's data is the wide range of data modalities such as text, network links, and categoric…
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.
Study classifies graphs with positive curvature without quadrilaterals.
problem Classifying graphs with positive Lin-Lu-Yau curvature without quadrilaterals.
method Definition of Ricci curvature on graphs, limit-free formulation using graph Laplacian.
result Identifies all simple connected C4-free graphs with positive Lin-Lu-Yau curvature.
Bayesian neural network predicts planetary instability.
problem Predicting planetary instability in compact systems.
method Novel Bayesian neural network trained on raw orbital elements.
result Model predicts planetary instability times with high accuracy and robust generalization.
FATE predicts user engagement on social apps with explainable explanations.
problem Accurate user engagement prediction for social apps with explainability.
method FATE, a flexible neural framework incorporating friendships, actions, and temporal dynamics.
result FATE outperforms state-of-the-art approaches by 10% error and 20% runtime reduction.
As research into community finding in social networks progresses, there is a need for algorithms capable of detecting overlapping community structure. Many algorithms have been proposed in recent years that are capable of assigning each node to more than a single community. The performance of these algorithms tends to …
Bayesian method for discovering hidden networks from excitatory point process data.
problem Discovering hidden networks from indirect excitatory point process data.
method Bayesian approach, Hawkes process, stochastic variational inference (SVI).
result Efficient SVI algorithm scales to long sequences of observations.
Study financial impact of Star Wars events on banking systems.
problem Financial crisis following destruction of Death Stars and galactic collapse.
method Calibrated model of banking and financial systems, systemic risk measurement.
result Rebel Alliance needs financial reserves to prevent crisis.
Method extracts taint flows to classify Bitcoin mining pools.
problem Understanding pseudonymous Bitcoin actors and their transactions.
method Taint analysis and graph embedding methods applied to taint flows.
result Taint flows from the same period show high similarity.
Link prediction is one of the fundamental problems in network analysis. In many applications, notably in genetics, a partially observed network may not contain any negative examples of absent edges, which creates a difficulty for many existing supervised learning approaches. We develop a new method which treats the obs…
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.
Trees are friendly to paths if they contain a path with all high-degree vertices.
problem Friendliness between trees and paths.
method Analyzing trees with paths and proving conditions for friendliness.
result A tree is friendly to a path if it contains a path with all vertices of degree greater than 2.
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.
Cryptocurrency markets exhibit violent, synchronised drawdowns, challenging diversification claims.
problem Cryptocurrency markets' violent drawdowns challenge diversification claims.
method Dynamic conditional tail dependence analysis
result Near-complete and stable lower-tail graph, upper tail that thins over time, dissolution of token categories into a core.
A test for comparing large random graphs based on network statistics.
problem Comparing friendship networks on Facebook and LinkedIn.
method General principle for two-sample hypothesis testing based on concentration of network statistics.
result A consistent two-sample test that is minimax optimal for certain network statistics.
Method finds interestingly dense subgroup connections in graphs.
problem Understanding patterns in graph connectivity based on node attributes.
method Information-theoretic definition of interestingness for subgroup connections.
result Identifies pairs of node subgroups with high or low edge density.
ALPINE predicts links in networks by querying the most informative pairs.
problem Predicting links in partially observed networks efficiently.
method Active learning using network embedding.
result ALPINE achieves significant link prediction accuracy with fewer queries.
Model infers temporal connections in dynamic graphs from node interactions.
problem Challenges in reasoning about evolving graphs, especially with human-specified edges.
method Temporal point processes and variational autoencoders with bilinear interactions.
result Model outperforms baselines and infers semantically interpretable connections.
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.
New approach learns latent motifs in networks for mesoscale structure analysis.
problem Understanding large-scale behavior in complex systems through mesoscale structures.
method Network dictionary learning (NDL) combining network sampling and nonnegative matrix factorization.
result Networks can be approximated using a small set of latent motifs.
InfDetect detects e-commerce insurance fraud using graph analysis.
problem Detecting fraudulent claims in e-commerce insurance with multiple parties involved.
method Developed a large-scale fraud detection system InfDetect using graph-based approaches.
result InfDetect successfully detected thousands of fraudulent claims and saved money daily.
LNMC improves link prediction on social networks by considering log-normal degree distributions.
problem Link prediction in social networks with log-normal degree distributions.
method Log-Normal Matrix Completion (LNMC) using Alternating Direction Method of Multipliers.
result Up to 5% AUC increase over non-structured sparsity based methods.
The paper proposes trading grades in a financial market to address unintended consequences of grading systems.
problem Unintended consequences of grading systems, such as unfair advantages and misaligned incentives.
method A thought experiment in a financial market structure to trade grades, similar to interest rate swaps.
result Grades should be viewed as personal equity, not used for selection criteria.
Joint analysis of data from multiple sources has the potential to improve our understanding of the underlying structures in complex data sets. For instance, in restaurant recommendation systems, recommendations can be based on rating histories of customers. In addition to rating histories, customers' social networks (e…
Graph matching in noisy environments with Markovian errors.
problem Graph matching under time-dependent Markovian noise.
method Introduced edgelighter error model and analyzed graph matching thresholds.
result Graph matching thresholds and mixing times are of order Θ(n2logn) for Erdős-Rényi graphs, and O(nαlogn) for Stochastic Block Model graphs. The paper studies a method to sample nodes from a massive graph using personalized PageRank.
problem Sampling from a massive network is expensive and impractical; the paper provides an alternative.
method The paper introduces a crawling method to approximate the personalized PageRank vector without querying the entire graph.
result The adjusted personalized PageRank vector can effectively select nodes within the same block as the seed node.
Enhances community detection in correlated networks with node attributes.
problem Community detection in multiple networks with correlated node attributes and edges.
method Introduced the correlated Contextual Stochastic Block Model (CSBM), developed a two-step matching procedure.
result Algorithm recovers exact node correspondence, enabling enhanced community detection.
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.
TGCN learns from multi-relational graphs, improving SSL performance.
problem Scalable semi-supervised learning from multi-relational data.
method Tensor-graph convolutional network with dynamic weights and graph-based regularizers.
result Significantly improved SSL performance over standard GCNs.
We present a simple agent-based model to study the development of a bubble and the consequential crash and investigate how their proximate triggering factor might relate to their fundamental mechanism, and vice versa. Our agents invest according to their opinion on future price movements, which is based on three source…
LEAP predicts graph edges and weights from path aggregations.
problem Predicting edges and weights in graphs.
method Trainable framework based on path aggregations.
result LEAP outperforms state-of-the-art methods in link and rating prediction.
In this paper, we consider the matrix completion problem when the observations are one-bit measurements of some underlying matrix M, and in particular the observed samples consist only of ones and no zeros. This problem is motivated by modern applications such as recommender systems and social networks where only "like…
Almost universally, wealth is not distributed uniformly within societies or economies. Even though wealth data have been collected in various forms for centuries, the origins for the observed wealth-disparity and social inequality are not yet fully understood. Especially the impact and connections of human behavior on …