Differentially private graph learning via bounded sensitivity PPR.
problem Protecting user data in graph learning algorithms.
method Proposes a sensitivity-bounded personalized PageRank (PPR) algorithm.
result Achieves similar accuracy to non-private algorithms with large degrees.
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.
The paper finds Koopman invariant subspaces using personalized PageRank.
problem Selecting a finite dictionary of observables for Koopman-invariant span.
method Exploiting zero-block structure in EDMD matrices and applying PageRank.
result Personalized PageRank can detect Koopman invariant subspaces.
This work analyzes PPR-based node embeddings and their topological information.
problem Understanding and interpreting PPR-based node embeddings.
method Unified framework and two methods for topology recovery.
result PPR-based embeddings maintain more topological information than random walk-based embeddings.
The paper analyzes oversmoothing in GNNs and quantifies the effects of mixing and denoising.
problem Oversmoothing in Graph Neural Networks (GNNs).
method Non-asymptotic analysis of graph convolutions and effects of mixing and denoising.
result The number of layers required for oversmoothing to occur is O(logN/log(logN)) for dense graphs. New algorithm improves fraud detection by analyzing financial account relationships.
problem High false positive rates and missed detections in conventional fraud detection systems.
method Personalized PageRank (PPR) algorithm to capture social dynamics of fraud.
result Integrating PPR enhances fraud detection model's predictive power.
This paper addresses graph embedding issues and introduces \strap for scalable, non-linear embeddings.
problem Preserving out-degree distributions and conflicting optimization goals on directed graphs.
method Introduces transpose proximity and \strap, a factorization-based algorithm that handles both directed and undirected graphs.
result Proposes \strap, which outperforms state-of-the-art methods in effectiveness and scalability.
Neural message passing algorithms for semi-supervised classification on graphs have recently achieved great success. However, for classifying a node these methods only consider nodes that are a few propagation steps away and the size of this utilized neighborhood is hard to extend. In this paper, we use the relationshi…
New algorithm assesses credit risk in multilayer networks over time.
problem Quantifying evolving credit risk in complex, interconnected networks.
method Personalized PageRank algorithm for multilayer networks.
result Credit risk evolves and propagates through multilayer networks over time.
CorePPR combines PageRank and CoreRank for scalable GNNs.
problem Expensive message passing in GNNs for large graphs.
method Learnable convex combination of approximate PPR and CoreRank, dynamic node selection.
result CorePPR outperforms PPRGo on large graphs.
This paper analyzes l1-regularized PageRank for local graph clustering, proving its effectiveness and efficiency.
problem Local graph clustering in large graphs, focusing on recovering a single target cluster given a seed node.
method Statistical analysis of l1-regularized PageRank method for recovery of a target cluster.
result l1-regularized PageRank recovers the full target cluster with bounded false positives and exactly the target cluster if the seed is connected solely to it.
Despite the overwhelming success of the existing Social Networking Services (SNS), their centralized ownership and control have led to serious concerns in user privacy, censorship vulnerability and operational robustness of these services. To overcome these limitations, Distributed Social Networks (DSN) have recently b…
Jiří Matoušek (1963-2015) had many breakthrough contributions in mathematics and algorithm design. His milestone results are not only profound but also elegant. By going beyond the original objects --- such as Euclidean spaces or linear programs --- Jirka found the essence of the challenging mathematical/algorithmic pr…
Novel compression method preserves privacy while reducing communication costs.
problem Reducing communication costs in differential privacy mechanisms.
method Poisson private representation (PPR) for compressing and simulating local randomizers.
result Achieves compression within a logarithmic gap from theoretical lower bound.
Community detection is, at its core, an attempt to attach an interpretable function to an otherwise indecipherable form. The importance of labeling communities has obvious implications for identifying clusters in social networks, but it has a number of equally relevant applications in product recommendations, biologica…
Recommendation systems are widely used by different user service providers specially those who have interactions with the large community of users. This paper introduces a recommender system based on community detection. The recommendation is provided using the local and global similarities between users. The local inf…
Estimates mode of discrete distributions with fewer samples.
problem Identifying the mode of a discrete distribution with high probability.
method Generalizes PPR martingale confidence sequences to handle multiple modes.
result PPR-1v1 stopping rule is asymptotically optimal and significantly more efficient.
Graph diffusion convolution improves graph learning by leveraging generalized graph diffusion.
problem Noisy and arbitrarily defined edges in real graphs.
method Graph diffusion convolution (GDC) using generalized graph diffusion like heat kernel and personalized PageRank.
result Replacing message passing with graph diffusion convolution leads to significant performance improvements.
HGP tackles noisy connections and heterogeneity in social graphs for better social recommendation.
problem Challenges in social graphs, especially noisy connections and heterogeneity, hinder GNN-based social recommendation.
method HGP uses a tripartite graph to reduce complexity, personalized PageRank for node embeddings, and attention mechanism for integration.
result HGP outperforms baselines in AUC and F1-score metrics on a large-scale dataset.
A framework combines multiple types of data for better item recommendations.
problem Limited performance of top-N recommendation systems using only one or two types of information.
method Design and implement GraFC2T2, a graph-based framework that encodes and combines content, temporal, and trust information.
result Combining different types of information improves recommendation performance.
The paper analyzes how quantum PageRank changes with small perturbations.
problem Estimating sensitivity of quantum PageRank to small changes.
method Finite dimensional perturbation theory to estimate changes and bounds.
result Estimation of lower bound of convergence radius and error bounds.
Develops PageRank for directed hypergraphs using metabolic network.
problem Lack of directed hypergraph datasets for PageRank algorithm.
method Developed PageRank algorithm for directed hypergraphs and applied it to metabolic network.
result Successfully applied novel PageRank algorithm to metabolic network.
Generalizes k-means to graphs using PageRank.
problem Clustering nodes in directed and undirected graphs.
method Utilizes PageRank to compute node centrality in graphs.
result Robustly computes centrality in graphs and metric spaces.
FREDE efficiently embeds graphs using linear space and guarantees quality.
problem Efficiently embedding graphs with quality guarantees and linear space complexity.
method FREDE combines matrix sketching with a nonlinear transform of PageRank similarities to achieve linear space and quality guarantees.
result FREDE provides column-covariance approximation guarantees that are nearly as good as SVD, even with limited node similarities.
The article uses PageRank and persistent homology for scalable graph comparison.
problem Comparing the similarities between complex networks.
method Combines PageRank and persistent homology to compute a scalable graph descriptor.
result Shows the effectiveness of the method on shape mesh datasets.
The importance of nodes in a network constantly fluctuates based on changes in the network structure as well as changes in external interest. We propose an evolving teleportation adaptation of the PageRank method to capture how changes in external interest influence the importance of a node. This framework seamlessly g…
This paper shows that pairwise PageRank orders emerge from two-hop walks. The main tool used here refers to a specially designed sign-mirror function and a parameter curve, whose low-order derivative information implies pairwise PageRank orders with high probability. We study the pairwise correct rate by placing the Go…
PageRank for Semi-Supervised Learning has shown to leverage data structures and limited tagged examples to yield meaningful classification. Despite successes, classification performance can still be improved, particularly in cases of fuzzy graphs or unbalanced labeled data. To address such limitations, a novel approach…
Introduces NCDawareRank, a new ranking framework for networks.
problem The overlooked teleportation component in Random Surfer model.
method Exploits network meta-information and higher-order structural organization.
result NCDawareRank preserves PageRank's mathematical structure and computational characteristics.
Study models contagion in Bitcoin network using Google matrices.
problem Analyzing financial contagion in Bitcoin network.
method Constructed Google matrices, calculated PageRank and CheiRank, modeled bankruptcy threshold.
result Phase transition at κ≈0.1, showing bankruptcy for most users.
Study uses Google matrix analysis to show how COVID-19 changed international trade flows.
problem Impact of COVID-19 on international trade patterns.
method Google matrix analysis of World Trade Network (WTN), including PageRank, CheiRank, and reduced Google matrix.
result Significant changes in international trade flows due to the pandemic, affecting export and import balances.
We introduce a new convex optimization problem, termed quadratic decomposable submodular function minimization (QDSFM), which allows to model a number of learning tasks on graphs and hypergraphs. The problem exhibits close ties to decomposable submodular function minimization (DSFM), yet is much more challenging to sol…
Diffusion-based classifiers such as those relying on the Personalized PageRank and the Heat kernel, enjoy remarkable classification accuracy at modest computational requirements. Their performance however is affected by the extent to which the chosen diffusion captures a typically unknown label propagation mechanism, t…
Optimizes deep learning pipelines with novel algorithms for smooth and non-smooth functions.
problem Optimizing deep learning pipelines for smooth and non-smooth functions.
method Provided matching lower and upper bounds for smooth convex and non-convex functions, and developed PPRS for non-smooth convex functions.
result PPRS achieves near-linear speed-up and convergence time for non-smooth non-convex problems.
For researching the association between coal enterprise management and return in financial market, this paper applies the method of time difference relevance and PageRank method to seek the leader-index of a stock set containing 21 coal enterprises in A-share market and score those stocks. Based on the return in 2011, …
Reinforcement learning has gained wide popularity as a technique for simulation-driven approximate dynamic programming. A less known aspect is that the very reasons that make it effective in dynamic programming can also be leveraged for using it for distributed schemes for certain matrix computations involving non-nega…
PPRGo uses approximate PageRank to speed up GNNs on large graphs.
problem Efficiently learning on large graphs using GNNs.
method Approximates PageRank for efficient information diffusion in GNNs.
result PPRGo outperforms other methods in speed and scalability.
InstantEmbedding efficiently generates node representations with less computation and memory.
problem Efficiently generating local node representations for large graphs.
method Local PageRank computations in sublinear time.
result Significantly faster and less memory-intensive than traditional methods.
Repelling random walks improve graph-based sampling efficiency.
problem Efficient graph-based sampling and statistical estimation.
method Induces correlations between trajectories of an ensemble of walkers on a graph, maintaining unbiasedness.
result Improves concentration of statistical estimators on graphs.
Adaptive GPR-GNN optimizes node feature and topology learning.
problem Optimizing GNNs for both node features and graph topology, regardless of homophily or heterophily.
method Adaptive Universal Generalized PageRank (GPR) Graph Neural Network (GPR-GNN) that learns optimal GPR weights.
result Significant performance improvement on node classification tasks compared to state-of-the-art GNNs.
Study identifies transitions between traffic modes on Cologne motorways.
problem Understanding transitions between different traffic modes.
method Constructed state transition network, identified dominant states using PageRank algorithm.
result Identified seasonal dependence in traffic modes.
Unsupervised scheme ranks sentences in text documents based on semantic importance.
problem Ranking sentences in text documents without labeled data.
method Extracts essential words and phrases, constructs semantic phrase and sentence graphs, applies PageRank, combines scores, and optimizes for topic diversity.
result SSR outperforms individual judges and compares favorably with combined rankings on benchmarks.
Mapper merges GNNs with TDA for graph visualisation.
problem Visualising complex graphs for understanding model structure.
method Combining Mapper, GNNs, and PageRank for graph pooling.
result Hierarchical visualisations reveal graph structure and model application.
Study lead-lag relationships in foreign exchange markets using three approaches.
problem Lack of research on lead-lag relationships in foreign exchange markets.
method Three approaches: lagged correlations, lagged partial correlations, and Granger causality.
result Statistically significant lead-lag relationships found in some exchange rate pairs.
Deep learning is a form of machine learning for nonlinear high dimensional pattern matching and prediction. By taking a Bayesian probabilistic perspective, we provide a number of insights into more efficient algorithms for optimisation and hyper-parameter tuning. Traditional high-dimensional data reduction techniques, …
The paper explores how machine learning personalization aligns with humanistic views of the person.
problem Aligning machine learning personalization with humanistic views of the person.
method Explicating the concept of personalization and contrasting it with humanistic views of the person.
result Proposes dimensions for evaluating the degree of personalization of ML personalized scores.
This work identifies and mitigates topological bias in HGNNs using meta-weighting and debiasing.
problem Topological bias in HGNNs affects model performance on specific nodes.
method Meta-weighting adjacency matrix, PageRank projection, debiasing structure.
result The debiasing structure improves HGNNs' performance and debiasing.
Federated learning framework extended for personalizing global models.
problem Evaluate personalization strategies for global models on-device.
method Extend federated learning framework, develop tools for analysis and evaluation.
result Personalization yields significant benefits for a large user population.