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Introduction to Special Issue on the Best Papers from KDD 2016
Charu C. Aggarwal
Article No.: 39
This issue contains the best papers from the ACM KDD Conference 2016. As is customary at KDD, special issue papers are invited only from the research track. The top-ranked papers from the KDD 2016 conference are included in this issue. This issue...
Ranking Causal Anomalies for System Fault Diagnosis via Temporal and Dynamical Analysis on Vanishing Correlations
Wei Cheng, Jingchao Ni, Kai Zhang, Haifeng Chen, Guofei Jiang, Yu Shi, Xiang Zhang, Wei Wang
Article No.: 40
Detecting system anomalies is an important problem in many fields such as security, fault management, and industrial optimization. Recently, invariant network has shown to be powerful in characterizing complex system behaviours. In the invariant...
Cross-Dependency Inference in Multi-Layered Networks: A Collaborative Filtering Perspective
Chen Chen, Hanghang Tong, Lei Xie, Lei Ying, Qing He
Article No.: 42
The increasingly connected world has catalyzed the fusion of networks from different domains, which facilitates the emergence of a new network model—multi-layered networks. Examples of such kind of network systems include critical...
TRIÈST: Counting Local and Global Triangles in Fully Dynamic Streams with Fixed Memory Size
Lorenzo De Stefani, Alessandro Epasto, Matteo Riondato, Eli Upfal
Article No.: 43
“Ogni lassada xe persa.”1-- Proverb from Trieste, Italy.
Graph-Based Fraud Detection in the Face of Camouflage
Bryan Hooi, Kijung Shin, Hyun Ah Song, Alex Beutel, Neil Shah, Christos Faloutsos
Article No.: 44
Given a bipartite graph of users and the products that they review, or followers and followees, how can we detect fake reviews or follows? Existing fraud detection methods (spectral, etc.) try to identify dense subgraphs of nodes that are sparsely...
Section: Special Issue on KDD 2016
Discovering Conditional Matching Rules
Yihan Wang, Shaoxu Song, Lei Chen, Jeffrey Xu Yu, Hong Cheng
Article No.: 46
Matching dependencies (MDs) have recently been proposed to make data dependencies tolerant to various information representations, and found useful in data quality applications such as record matching. Instead of the strict equality...
Query-Driven Learning for Predictive Analytics of Data Subspace Cardinality
Christos Anagnostopoulos, Peter Triantafillou
Article No.: 47
Fundamental to many predictive analytics tasks is the ability to estimate the cardinality (number of data items) of multi-dimensional data subspaces, defined by query selections over datasets. This is crucial for data analysts dealing with, e.g.,...
Large-Scale Online Feature Selection for Ultra-High Dimensional Sparse Data
Yue Wu, Steven C. H. Hoi, Tao Mei, Nenghai Yu
Article No.: 48
Feature selection (FS) is an important technique in machine learning and data mining, especially for large-scale high-dimensional data. Most existing studies have been restricted to batch learning, which is often inefficient and poorly scalable...
Modeling Temporal Activity to Detect Anomalous Behavior in Social Media
Alceu Ferraz Costa, Yuto Yamaguchi, Agma Juci Machado Traina, Caetano Traina Jr., Christos Faloutsos
Article No.: 49
Social media has become a popular and important tool for human communication. However, due to this popularity, spam and the distribution of malicious content by computer-controlled users, known as bots, has become a widespread problem. At the same...
Rumor Gauge: Predicting the Veracity of Rumors on Twitter
Soroush Vosoughi, Mostafa ‘Neo’ Mohsenvand, Deb Roy
Article No.: 50
The spread of malicious or accidental misinformation in social media, especially in time-sensitive situations, such as real-world emergencies, can have harmful effects on individuals and society. In this work, we developed models for automated...
Mining Community Structures in Multidimensional Networks
Oualid Boutemine, Mohamed Bouguessa
Article No.: 51
We investigate the problem of community detection in multidimensional networks, that is, networks where entities engage in various interaction types (dimensions) simultaneously. While some approaches have been proposed to identify community...
Real-Time Large-Scale Map Matching Using Mobile Phone Data
Essam Algizawy, Tetsuji Ogawa, Ahmed El-Mahdy
Article No.: 52
With the wide spread use of mobile phones, cellular mobile big data is becoming an important resource that provides a wealth of information with almost no cost. However, the data generally suffers from relatively high spatial granularity, limiting...