Advances in Knowledge Discovery and Data Mining

Name: Advances in Knowledge Discovery and Data Mining | 22nd Pacific-Asia Conference, PAKDD 2018, Melbourne, VIC, Australia, June 3-6, 2018, Proceedings, Part III
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22nd Pacific-Asia Conference, PAKDD 2018, Melbourne, VIC, Australia, June 3-6, 2018, Proceedings, Part III

Dinh Phung Vincent S. Tseng Geoffrey I. Webb Bao Ho Mohadeseh Ganji Lida Rashidi(Herausgeber*in)

Springer (Verlag)

Erschienen am 16. Juni 2018

XXVIII, 835 Seiten

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Inhalt

Intro
PC Chairs' Preface
General Chairs' Preface
Organization
Contents - Part III
Feature Learning and Data Mining Process
Discovering High Utility Itemsets Based on the Artificial Bee Colony Algorithm
Abstract
1 Introduction
2 Preliminaries
2.1 Problem of HUI Mining
2.2 ABC Algorithm
3 Mining HUIs Using the ABC
3.1 Bitmap Item Information Representation
3.2 Modeling HUI Discovery Using the ABC
3.3 Direct Nectar Source Generation for Scout Bees
3.4 Algorithm Description
4 Performance Evaluation
4.1 Experimental Environment and Datasets
4.2 Running Time
4.3 Number of Discovered HUIs
4.4 Convergence
5 Conclusions
Acknowledgments
References
A Scalable and Efficient Subgroup Blocking Scheme for Multidatabase Record Linkage
1 Introduction
2 Related Work
3 Subgroup Blocking Process
3.1 Potential Candidate Grouping
3.2 Candidate Graph Construction
3.3 Subgroup Candidate Generation
4 Analysis of Subgroup Blocking
5 Experiments and Discussion
6 Conclusions and Future Work
References
Efficient Feature Selection Framework for Digital Marketing Applications
1 Introduction
2 Related Work
3 Overall Framework
4 Feature Exploration Using Semantic Ranking and Generative Filtering
4.1 The Semantic Ranking Guided Feature Selection Algorithm
4.2 Combining with Generative Filtering for Better Performance
5 Progressive Sampling and Feature Selection Framework
5.1 Coarse to Fine Implementation
5.2 Time and Space Reduction Through Progressive Sampling
6 Experiments and Discussion
7 Conclusion
References
Dynamic Feature Selection Algorithm Based on Minimum Vertex Cover of Hypergraph
1 Introduction
2 Preliminaries
3 Dynamic Feature Selection Algorithm Based on Minimum Vertex Cover of Hypergraph
3.1 The Induced Hypergraph
3.2 Updating Minimum Vertex Cover of Hypergraph
3.3 Dynamic Feature Selection Algorithm
4 Experimental Analysis
5 Conclusions
References
Feature Selection for Multiclass Binary Data
1 Introduction
2 Related Work
3 Preliminary Concepts
4 Problem Formulation
5 Our Approach
5.1 Measuring the Sparse Value Distribution
5.2 New Feature Selection Objective
5.3 A Greedy Feature Selection Approach
6 Evaluation
6.1 Experimental Results
6.2 Evaluation Insights
7 Conclusion
References
Scalable Model-Based Cascaded Imputation of Missing Data
1 Introduction
2 Related Work
3 Proposed Method
4 Methodology
5 Discussion of Experimental Results
6 Conclusions
References
On Reducing Dimensionality of Labeled Data Efficiently
1 Introduction
2 Related Work
2.1 Metric Learning
2.2 Nonlinear Algorithms for Collapsing Classes
2.3 Parametric Embedding
3 Nonlinear Parametric Embedding
4 Evaluation
4.1 Experiment Settings
4.2 Results
5 Conclusion
References
Using Metric Space Indexing for Complete and Efficient Record Linkage
1 Introduction
2 Related Work
3 Approach
4 Experiments and Results
4.1 Cora Results
4.2 Demographic Dataset Results
5 Conclusions and Future Work
References
Dimensionality Reduction via Community Detection in Small Sample Datasets
1 Introduction
2 Related Work
3 Proposed Approach
4 Experimental Setup and Results
4.1 Experimental Setup
4.2 Results
UCI Machine Learning Repository Benchmarks.
Empirical Evaluation of FeatureNet in the
Why We Achieve Performance Gains?
Computational Sustainability - A Case Study and New AI Datasets.
Note on Scalability.
5 Conclusion and Future Work
References
An Interaction-Enhanced Feature Selection Algorithm
1 Introduction
2 Proposed Method for Feature Selection
2.1 An Information Measure for Feature Interaction
2.2 An Interaction Based Feature Selection Method
2.3 Complexity Analysis
3 Experiments
3.1 Overall Performance
3.2 Performance with Respect to the Number of Features
4 Discussion and Conclusion
References
An Extended Random-Sets Model for Fusion-Based Text Feature Selection
1 Introduction
2 Related Work
3 Problem Formulation and Background
3.1 Latent Dirichlet Allocation and Limitations
3.2 Extending Random-Sets
4 The Proposed SIF2 Model
4.1 Fusing Hierarchical Features
5 Evaluation
5.1 Dataset and Evaluation Measures
5.2 Baseline Models and Settings
5.3 Experimental Design
5.4 Results
5.5 Discussion
6 Conclusions
References
Attribute Reduction Algorithm Based on Improved Information Gain Rate and Ant Colony Optimization
Abstract
1 Introduction
2 Preliminary
2.1 Rough Set Theory
2.2 Information Representation in Decision Table
3 Ant Colony Optimization for Attribute Reduction
3.1 Ant Colony Optimization Principle
3.2 Local Solution
3.3 Pheromone Updating
3.4 The Proposed Algorithm
4 Experimental Analysis
4.1 Comparison with Other Methods
4.2 Analysis of Convergence Rate
5 Conclusion
Acknowledgements
References
Efficient Approximate Algorithms for the Closest Pair Problem in High Dimensional Spaces
1 Introduction
2 Proposed Approximate Algorithms
2.1 ACP-D
2.2 ACP-P
3 Experiments
4 Conclusions
4.1 Proof of Theorem1
4.2 Proof of Corollary1
4.3 Proof of Theorem2
4.4 Proof of Corollary2
References
Efficient Compression Technique for Sparse Sets
1 Introduction
1.1 Revisiting Compression Scheme of KulkarniP16
1.2 Our Result
1.3 Comparison Between BCS and Minhash and Its Variants
1.4 Applications of Our Result
2 Analysis
3 Experimental Evaluation
3.1 Results on Synthetic Data
3.2 Results on Real-World Data
4 Concluding Remarks and Open Questions
References
It Pays to Be Certain: Unsupervised Record Linkage via Ambiguity Minimization
1 Introduction
2 Related Work
3 Problem Definition
3.1 Evaluating Record Linkage Scoring
4 Our Method
4.1 The Scoring Formulation
4.2 Developing the Objective Function
4.3 Optimization Formulation
4.4 Overall Approach
5 Experiments and Results
5.1 Experimental Setup
5.2 Comparative Evaluation of Record Pairs Ordering
5.3 Further Analysis of Our Method
6 Conclusions
References
Community Detection and Network Science
Consensus Community Detection in Multilayer Networks Using Parameter-Free Graph Pruning
1 Introduction
2 Background
2.1 Generative Models for Graph Pruning
2.2 Ensemble-Based Multilayer Community Detection
3 EMCD and Parameter-Free Graph Pruning
4 Evaluation Methodology
5 Results
5.1 Impact of Model-Filters on M-EMCD*
5.2 Evaluation with Competing Methods
6 Conclusion
References
Community Discovery Based on Social Relations and Temporal-Spatial Topics in LBSNs
1 Introduction
2 Related Works
3 Community Discovery Model
3.1 Preliminaries
3.2 Basic Idea of SRTST Model
3.3 Model Construction
4 Parameter Estimation
5 Experiments
5.1 LBSN Datasets and Evaluation Metrics
5.2 Parameter Configuration
5.3 Comparison Algorithms
5.4 Experimental Results
6 Conclusion
References
A Unified Weakly Supervised Framework for Community Detection and Semantic Matching
1 Introduction
2 Proposed WSCDSM Framework
2.1 Modeling TC Communities
2.2 Modeling SC Communities
2.3 The Unified Model: Matching TC with SC Communities
3 Optimization
4 Experimental Results
4.1 The Performance of Community Detection
4.2 The Matching Between Semantic and Communities
5 Conclusion
References
Tapping Community Memberships and Devising a Novel Homophily Modeling Approach for Trust Prediction
1 Introduction
2 Related Work
3 Inferring Trust Using Community Memberships and Novel Homophily Modeling
3.1 Problem Setting
3.2 Encoding Community Membership Information for Trust Prediction
3.3 Proposed Homophily Modeling Using Users' Item Ratings
4 The Overall Optimization Framework
4.1 Trust Inference Using the Community-Based Factor
4.2 Trust Inference Through Homophily Modeling Using Users' Item Ratings
4.3 The chTrust Algorithm
5 Experiments
5.1 Datasets
5.2 Evaluation Metric and Experiment Settings
5.3 Baselines
5.4 Results
6 Conclusion
References
Deep Learning Theory and Applications in KDD
Text-Visualizing Neural Network Model: Understanding Online Financial Textual Data
1 Introduction
1.1 Motivation and Purpose
1.2 Main Approach and Problem Settings
2 Importance of Infiltration (II) Algorithm
2.1 Setup of NN Model
2.2 Initialization and Learning of Parameters
2.3 Proposed and Baseline Models
3 Text Visualization Demonstration Using Real Data
3.1 Dataset and Model Development
3.2 Interpretability Evaluation
3.3 Clustering Interpretability Evaluation
3.4 Market Mood Predictability Evaluation
3.5 Text Visualization
4 Related Work
5 Conclusion
A Theoretical Analysis of the II Algorithm
References
MIDA: Multiple Imputation Using Denoising Autoencoders
1 Introduction
2 Background
2.1 Missing Data
2.2 Autoencoders and Denoising Autoencoders
3 Models
3.1 Our Model
3.2 Competitors and Comparison
4 Experiments
4.1 Datasets
4.2 Inducing Missingness
4.3 Main Results
4.4 Increased Missingness Proportion
4.5 Impact on Final Analysis
5 Conclusion
References
Dual Control Memory Augmented Neural Networks for Treatment Recommendations
1 Introduction
2 Methods
2.1 Problem Formulation
2.2 DNC Overview
2.3 Proposed Model
3 Results
3.1 Synthetic Task: Odd-Even Sequence Prediction
3.2 Treatment Recommendation Tasks
4 Related Works
5 Conclusion
References
Denoising Time Series Data Using Asymmetric Generative Adversarial Networks
1 Introduction
2 Related Work
3 Problem Definition
4 Asymmetric Generative Adversarial Network
4.1 Generator and Discriminator Network Architecture
5 Experimental Setup and Results
5.1 Synthetic Dataset
5.2 EEG Dataset
6 Conclusion
References
Shared Deep Kernel Learning for Dimensionality Reduction
1 Introduction
2 Related Works
2.1 GP and GPLVM
2.2 DKL
3 The Proposed Model
4 Experiments
5 Conclusion
References
CDSSD: Refreshing Single Shot Object Detection Using a Conv-Deconv Network
1 Introduction
2 Limitations of Related Work
3 CDSSD Architecture
3.1 SSD
3.2 Unsupervised Pretraining
3.3 Combining Feature Maps
3.4 Box Pooling: Reducing the Number of Default Boxes
4 Results
4.1 Training
4.2 PASCAL VOC
4.3 Ablation Study
4.4 MSCOCO
5 Conclusion
References
Binary Classification of Sequences Possessing Unilateral Common Factor with AMS and APR
1 Introduction
2 Related Work
3 Our Proposals
3.1 Network Architecture and Notations
3.2 Adaptive Multi-scale Sampling (AMS)
3.3 Activation Pattern Regularization (APR)
4 Experiments
4.1 Experimental Setup
4.2 Experiments on Dataset 1
4.3 Experiments on Dataset 2
4.4 Analyzing AMS and APR
5 Conclusion
References
Automating Reading Comprehension by Generating Question and Answer Pairs
1 Introduction
2 Problem Formulation
3 Related Work
4 Approach and Contributions
5 Answer Selection and Encoding
5.1 Named Entity Selection
5.2 Answer Selection Using Pointer Networks
6 Question Generation
6.1 Sequence to Sequence Model
Question Decoder:
6.2 Linguistic Features
7 Implementation Details
8 Experiments and Results
8.1 Results and Analysis
9 Conclusion
References
Emotion Classification with Data Augmentation Using Generative Adversarial Networks
1 Introduction
2 Related Work
2.1 Generative Adversarial Networks
2.2 Data Augmentation
3 Data Augmentation Using CycleGAN
3.1 Cycle-Consistent Adversarial Networks
3.2 Class Imbalance and Data Manifold
4 Experimental Studies
4.1 Benchmark Datasets
4.2 Experimental Results
5 Conclusions and Discussions
References
Trans2Vec: Learning Transaction Embedding via Items and Frequent Itemsets
1 Introduction
2 Related Work
3 Framework
3.1 Problem Definition
3.2 Learning Transaction Embeddings Based on Items
3.3 Learning Transaction Embeddings Based on Frequent Itemsets
3.4 Trans2Vec Method for Learning Transaction Embeddings
4 Experiments
4.1 Datasets
4.2 Baselines
4.3 Evaluation Metrics
4.4 Parameter Settings
4.5 Results and Discussion
4.6 Parameter Sensitivity
5 Conclusion
References
Detecting Complex Sensitive Information via Phrase Structure in Recursive Neural Networks
1 Introduction
2 Complex Sensitive Information Detection
3 SPR - Sensitive Phrase Based RNN Model
3.1 Phrase Structure
3.2 Recursive Neural Networks with Phrase Structure
3.3 Training SPR
4 Evaluation
4.1 Evaluation Methodology and Data
4.2 Performance Evaluation for Complex Sensitive Information
4.3 Qualitative Analysis
5 Related Work
6 Conclusion
References
Clustering and Unsupervised Learning
A Distance Scaling Method to Improve Density-Based Clustering
1 Introduction
2 Related Work
3 The Problem of Varied Densities
4 A Distance Scaling Method for Density-Ratio Estimation
5 Empirical Evaluation
5.1 Clustering Performance
6 Conclusion
References
Neighbourhood Contrast: A Better Means to Detect Clusters Than Density
1 Introduction
2 Neighbourhood Contrast
2.1 Property of Neighbourhood Contrast
2.2 Estimating Neighbourhood Contrast
3 Improving DP with Neighbourhood Contrast
4 Neighbourhood Contrast Clustering
4.1 Core Points and Cluster Nexuses
4.2 Assigning Non-core Points
5 Experiments
6 Conclusions
References
Clustering of Multiple Density Peaks
1 Introduction
2 Preliminary Knowledge and Problem Definition
2.1 The Algorithm of DPC
2.2 The Problems and Related Approaches
3 The MDPC Approach
3.1 Find Seed Clusters
3.2 Merge Seed Clusters
3.3 Algorithm Complexity
4 Experiments
4.1 Synthetic Datasets
4.2 Real Datasets
5 Conclusion
References
A New Local Density for Density Peak Clustering
1 Introduction
2 Related Work
3 Methodology
3.1 A New Local Density
3.2 NDPC Algorithm
3.3 NDPC-AC Algorithm
4 Experiments
4.1 Comparison Scheme
4.2 Experimental Results on Benchmark Datasets
4.3 Visualization of Experimental Results on ORL Dataset
5 Conclusion
References
An Efficient Ranking-Centered Density-Based Document Clustering Method
Abstract
1 Introduction
2 Ranking-Centered Density Document Clustering (RDDC)
2.1 Obtaining Nearest Neighbors as Relevant Documents
2.2 Graph Based Clustering
2.3 Relevancy Based Clustering
3 Empirical Analysis
3.1 Accuracy Analysis
3.2 Scalability and Complexity Analysis
3.3 Sensitivity Analysis
4 Conclusion
References
Fast Manifold Landmarking Using Locality-Sensitive Hashing
1 Introduction
2 Background
2.1 Landmarking Manifolds with Gaussian Processes
2.2 Locality-Sensitive Hashing
2.3 Distance-Based Hashing
3 LSH for Finding Manifold Landmarks
3.1 Manifold Assumption and Locality-Sensitive Hashing
3.2 Supervised LSH (LSH-SC)
3.3 Supervised DBH (DBH-SC)
3.4 Landmark Selection Methods
3.5 Complexity Analysis
4 Experimental Results
4.1 Classification Using Landmark-Based Transformed Features
4.2 Impact of Approach Improvements
4.3 Qualitative Evaluation
4.4 Quantitative Evaluation of Landmark Quality
5 Conclusion
References
Equitable Conceptual Clustering Using OWA Operator
1 Introduction
2 Background
2.1 Formal Concepts and Conceptual Clustering
2.2 Equitable Multiagent Optimization
2.3 Equitable Aggregation Functions
2.4 Ordered Weighted Averages (OWA)
3 ILP Models
3.1 OWA ILP Models
3.2 Other ILP Models
4 Related Work
5 Experiments and Results
6 Conclusion
References
Unsupervised Extremely Randomized Trees
1 Introduction and Preliminaries
2 Unsupervised Extremely Randomized Trees
3 Empirical Evaluation
3.1 Optimization of Parameters
3.2 Comparative Evaluation of UET
4 Conclusion and Perspectives
References
Local Graph Clustering by Multi-network Random Walk with Restart
1 Introduction
2 Related Work
3 Problem Definitions
4 Methods
4.1 Random Walk on Single Network
4.2 Random Walk on Multi-network
4.3 Localized Algorithm for MRWR
5 Experiment
5.1 Datasets and Baseline Methods
5.2 Effectiveness Evaluation
5.3 Efficiency Evaluation
6 Conclusion
References
Scalable Approximation Algorithm for Graph Summarization
1 Introduction
2 Related Work
3 Problem Definition
4 Algorithm
5 Evaluation
6 Conclusion
References
Privacy-Preserving and Security
RIPEx: Extracting Malicious IP Addresses from Security Forums Using Cross-Forum Learning
1 Introduction
2 Our Forums and Datasets
3 Overview of RIPEx
3.1 The IP Identification Module
3.2 The IP Characterization Module
3.3 Transfer Learning with Cross-Seeding
4 Evaluation of Our Approach
5 Related Work
6 Conclusion
References
Pattern-Mining Based Cryptanalysis of Bloom Filters for Privacy-Preserving Record Linkage
1 Introduction
2 Background and Related Work
3 Pattern-Mining Based Cryptanalysis Attack
3.1 Identifying Co-occurring Bit Positions in Bloom Filters
3.2 Plain-Text Value Re-identification
4 Experiments and Results
5 Conclusions and Future Work
References
A Privacy Preserving Bayesian Optimization with High Efficiency
1 Introduction
2 Background
2.1 Bayesian Optimization
2.2 Differential Privacy
3 The Proposed Framework
3.1 Error Preserving Privacy Framework
3.2 The Proposed Algorithm
3.3 Discussion of Differentially Private Bayesian Optimization
4 Experiments
4.1 Experiment with Benchmark Functions
4.2 Experiment with Real Datasets
5 Conclusion
References
Randomizing SVM Against Adversarial Attacks Under Uncertainty
1 Introduction
2 Related Work
3 Problem Definition
4 Attack Model Design
4.1 Restrained Range Attack with Uncertainty
4.2 Distributional Range Attack with Uncertainty
5 Randomized SVMs Learning
5.1 Randomized SVM Against RRA
5.2 Randomized SVM Against RRAU
5.3 Randomized SVM Against DRAU
6 Experimental Evaluation
6.1 Comparison with Deterministic SVM-RRA
6.2 Comparative Study with Standard SVMs
6.3 SVM-DRAU with Different Norms
7 Conclusion
References
Recommendation and Data Factorization
One for the Road: Recommending Male Street Attire
1 Introduction
2 Data Collection
2.1 Surveys
2.2 Data Statistics and Analysis
3 Prediction Model
4 Recommendation Model - MalOutRec
4.1 Construction of Bipartite Graph
4.2 Traversal of the Graph
4.3 Refining Recommendation Using Positive Influence Factor
5 Experiments
5.1 Prediction Accuracy
5.2 MalOutRec - Performance Assessment
6 Conclusion
References
Context-Aware Location Annotation on Mobility Records Through User Grouping
1 Introduction
2 Preliminary
3 Method
3.1 Overview of CAUG
3.2 User Grouping
3.3 Feature Extraction
3.4 Venue Ranking
4 Experimental Study
4.1 Setup
4.2 Results
5 Related Work
6 Conclusion
References
A Joint Optimization Approach for Personalized Recommendation Diversification
1 Introduction
2 Problem Formulation
3 Personalized Diversification Algorithms
3.1 Personalized Diversification Algorithm by Greedy Re-ranking
3.2 Personalized Diversification Algorithm by Joint Optimization
4 Personalized Diversity Measure
4.1 Limitations of Existing Diversity Measures
4.2 Formulation of Personalized Diversity Measure
5 Experiments
5.1 Experiments on Algorithms
5.2 Experiments on Measures
6 Related Work
7 Conclusion
References
Personalized Item-of-Interest Recommendation on Storage Constrained Smartphone Based on Word Embedding Quantization
1 Introduction
2 Related Work
3 Problem Formulation
4 The WEQ Framework
4.1 Word Embedding Quantization
4.2 Bounding Angle Checking Mechanism
5 Experiment and Evaluation
5.1 Experimental Settings
5.2 Performance Results
6 Conclusion
References
Social Network, Ubiquitous Data and Graph Mining
Topic-Specific Retweet Count Ranking for Weibo
1 Introduction
2 Related Work
3 TSTR Framework
3.1 Consideration and Design
3.2 Candidate Tweet Filter
3.3 LSTM-AE
3.4 DAE
3.5 Tweet Ranker
3.6 Chinese Word Embedding
4 Experiments
5 Conclusion
References
Motif-Aware Diffusion Network Inference
1 Introduction
2 Related Work
3 Motif-Aware Diffusion Network Inference
3.1 Notations and Problem Formulation
3.2 Estimating Motif Pattern from Cascade Data
3.3 Motif Prior Regularization
3.4 Learning
3.5 Computational Complexity Analysis
4 Validations
4.1 Experiments on Synthetic Networks
4.2 Experiment on a Real-World Network
4.3 Experiment on Real-World Cascades
5 Conclusion
References
Tri-Fly: Distributed Estimation of Global and Local Triangle Counts in Graph Streams
1 Introduction
2 Related Work
3 Notations and Problem Definition
3.1 Notations (Table2)
3.2 Problem Definition
4 Proposed Method: Tri-Fly
4.1 Overview (Fig.1)
4.2 Detailed Algorithm (Algorithm1)
4.3 Bias and Variance Analyses
4.4 Time and Space Complexity Analyses
5 Experiments
5.1 Experimental Settings
5.2 Q1. Illustration of Our Theorems (Fig.2)
5.3 Q2. Performance (Fig.3)
6 Conclusion
References
WFSM-MaxPWS: An Efficient Approach for Mining Weighted Frequent Subgraphs from Edge-Weighted Graph Databases
1 Introduction
2 Background and Related Works
3 Our Proposed Algorithm
3.1 WFSM-MaxPWS Canonical Ordering of Subgraph
3.2 MaxPWS Pruning Technique
3.3 The WFSM-MaxPWS Algorithm
4 Experimental Results
5 Conclusions
References
A Game-Theoretic Adversarial Approach to Dynamic Network Prediction
1 Introduction
2 Related Work
3 Adversarial Dynamic Network Prediction
3.1 Adversarial Prediction Formulation
3.2 Performance Guarantees and Features
3.3 Relationship with TERGMs
4 Experiments
5 Conclusions
References
Targeted Influence Minimization in Social Networks
1 Introduction
2 Problem Definition
2.1 Diffusion Model
2.2 Targeted Influence Minimization
3 Budget Unconstrained Solution
4 Budget Constrained Solution
5 Sampling-Based Solution
5.1 Minimum Influence Path
5.2 Sampling-Based Greedy Algorithm
6 Experimental Study
6.1 Evaluation of Effectiveness
6.2 Evaluation of Efficiency
7 Conclusion
References
Maximizing Social Influence on Target Users
1 Introduction
2 Problem Formulation
3 Probabilistic Social Influence Model
3.1 Assigning Direct Weight
3.2 Social Influence Distribution
3.3 Objective Function
4 Cluster-Based Assembling Method
4.1 Cluster Detection Using Influence Behavior
4.2 Greedy Algorithm
5 Experiment Evaluation
5.1 Experiment Setup
5.2 Spread Achieved
5.3 Running Time
6 Conclusion
References
Team Expansion in Collaborative Environments
1 Introduction
2 Problem Statement
3 The Proposed Approach
3.1 The TECE Model
3.2 Generalizations and Discussions
4 Experiments
4.1 Experimental Setup
4.2 Experimental Results
5 Related Work
6 Conclusions
References
HashAlign: Hash-Based Alignment of Multiple Graphs
1 Introduction
2 Related Work
3 Proposed Formulation: Two-Graph Alignment
3.1 Definition: Relaxed Two-Graph Alignment Problem
3.2 Node Representation: Handling Node and Edge Attributes
3.3 Proposed Hashing-Based Computation of Potential Matchings
3.4 From Similarities to Matchings
4 HASHALIGN: Multiple Graph Alignment
5 Experimental Analysis
6 Conclusions
References
Evaluating and Analyzing Reliability over Decentralized and Complex Networks
1 Introduction
2 Main Definitions and Related Works
3 Agent-Based Reliability Estimation
3.1 Estimating Two-Terminal Reliability
3.2 Evaluation of the Graph Reduction Algorithm
4 Conclusion and Future Works
References
Efficient Exact and Approximate Algorithms for Computing Betweenness Centrality in Directed Graphs
1 Introduction
2 Preliminaries
3 Related Work
4 Computing Betweenness Centrality in Directed Graphs
4.1 Reachable Vertices
4.2 The Exact Algorithm
4.3 The Approximate Algorithm
5 Experimental Results
6 Conclusion
References
Forecasting Bitcoin Price with Graph Chainlets
1 Introduction
2 Related Work
3 Methodology
3.1 Graph Chainlets
3.2 Clustering Chainlets
4 Experiments
4.1 Granger Causality
4.2 Price Prediction
5 Conclusion
References
Information Propagation Trees for Protest Event Prediction
1 Introduction
2 Related Work
3 Preliminaries and Problem Setup
3.1 Definitions
3.2 Problem Setup
4 The Propagation Tree Framework
5 Experimental Settings and Results
5.1 Experiment Setup
5.2 Performance Metrics
5.3 Results and Discussions
6 Conclusion
References
Predictive Team Formation Analysis via Feature Representation Learning on Social Networks
1 Introduction
2 Problem Statement
3 Proposed Methods
3.1 Learning Node Representation (n2v) for Team Formation
3.2 The Biased-n2v Method
3.3 The Guided-n2v Method
4 Experimental Results
5 Related Work
6 Conclusions
References
Leveraging Local Interactions for Geolocating Social Media Users
1 Introduction
2 Related Work
2.1 Text-Based Methods
2.2 Network-Based Methods
2.3 Hybrid Methods
3 Data
4 The Proposed Approach
4.1 Construction of Social Graph
4.2 Predicting Geographical Proximity from Linguistic Similarity
4.3 Predicting Locations of Isolated Users
4.4 Label Propagation with Modified Adsorption
5 Experimental Results
5.1 Experiment Setting
5.2 Evaluation Metrics
5.3 Results
5.4 Other Textual Similarity Measures
6 Conclusion and Future Work
References
Utilizing Sequences of Touch Gestures for User Verification on Mobile Devices
Abstract
1 Introduction
2 Related Work
3 Attack Scenario
4 Suggested Approach
4.1 Data Acquisition
4.2 Feature Extraction
4.3 Verification
5 Experiment Setup
5.1 Data Collection
5.2 Evaluation Metrics
5.3 Hyper Parameter Tuning
6 Experiments and Results
6.1 Outsider and Insider Attack Scenario
6.2 Response Time and Comparison with State-of-the-Art
7 Conclusion
References
Author Index

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