NDLI: Finding the Needle in the Haystack: Identifying Business Communities in Internet Traffic

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WI 2014 Non-Program Committee Reviewers - I

Quantum Cognition

Structural Vulnerability Analysis of Overlapping Communities in Complex Networks

Cloud Application Development Methodology

Multi-agent Information Diffusion Model for Twitter

Adaptive Landmark Selection Strategies for Fast Shortest Path Computation in Large Real-World Graphs

Enumerating Communities for a Deeper Understanding of Community Finding

Big Data - Characterizing an Emerging Research Field Using Topic Models

Efficient Spatio-textual Similarity Join Using MapReduce

Aspect-Based Similar Entity Search in Semantic Knowledge Graphs with Diversity-Awareness and Relaxation

Learning Concise Pattern for Interlinking with Extended Version Space

SORM: A Social Opinion Relevance Model

Collecting Conceptualized Relations from Terabytes of Web Texts for Understanding Unknown Terms

Person Identification between Different Online Social Networks

Obtaining Technology Insights from Large and Heterogeneous Document Collections

A New Sentence Similarity Method Based on a Three-Layer Sentence Representation

Multi-feature and DAG-Based Multi-tree Matching Algorithm for Automatic Web Data Mining

A Comparison of Mobile Rule Engines for Reasoning on Semantic Web Based Health Data

Locality-Sensitive Hashing for Massive String-Based Ontology Matching

Semantic Heuristic Search in Collaborative Networks: Measures and Contexts

An Approach for Learning and Construction of Expressive Ontology from Text in Natural Language

Mining Twitter Data with Resource Constraints

Conjunctive Query Programming: A Paradigm for Knowledge Engineering of Optimization Problems in the Semantic Web

Finding the Needle in the Haystack: Identifying Business Communities in Internet Traffic

NCDREC: A Decomposability Inspired Framework for Top-N Recommendation

Optimizing Personalized Ranking in Recommender Systems with Metadata Awareness

Improving Personalized Ranking in Recommender Systems with Multimodal Interactions

A Link Prediction Approach for Item Recommendation with Complex Number

SemanticSVD++: Incorporating Semantic Taste Evolution for Predicting Ratings

Propagating Users' Similarity towards Improving Recommender Systems

Online Recommender System for Radio Station Hosting: Experimental Results Revisited

Fuzziness and Ontology in Personalization of Selection Processes in the Semantic Web

Sentence-Based Plot Classification for Online Review Comments

Summarizing Search Results with Community-Based Question Answering

Lazy Walks Versus Walks with Backstep: Flavor of PageRank

A Machine Learning Approach to SPARQL Query Performance Prediction

Improving Biodiversity Data Retrieval through Semantic Search and Ontologies

Online Retweet Recommendation with Item Count Limits

Extracting Attributes and Synonymous Attributes from Online Encyclopedias

Credibility Microscope: Relating Web Page Credibility Evaluations to Their Textual Content

Harnessing Social Signals to Enhance a Search

Relevant Sources of Information Are Not Necessarily Popular Ones

Hybrid Algorithm for Precise Recommendation from Almost Infinite Set of Websites

Combining Query Terms Extension and Weight Correlative for Expert Finding

Recommender System for Crowdsourcing Platform Witology

Feature Selection and Term Weighting

Improving Collaborative Filtering Based Recommenders Using Topic Modelling

Dynamic Learning of Keyword-Based Preferences for News Recommendation

Link Prediction Based on Multi-steps Resource Allocation

Fuzzy Subjective Sentiment Phrases: A Context Sensitive and Self-Maintaining Sentiment Lexicon

On the Effectiveness of Emergent Task Allocation of Virtual Programmer Teams

Identification of Opinion Leaders Based on User Clustering and Sentiment Analysis

Indirect Keyword Recommendation

Tweet Sentiment Analytics with Context Sensitive Tone-Word Lexicon

An Ontology for Guiding Performance Testing

Ontology as a Base for State Space of Expert Systems

Learning of Legal Ontology Supporting the User Queries Satisfaction

Multi-agent Based System for Multilingual Ontologies Maintenance

An Approach to Join Ontologies and Their Reuse in the Construction of Application Ontologies

Interpreting Discovered Patterns in Terms of Ontology Concepts

Detecting and Correlating Video-Based Event Patterns: An Ontology Driven Approach

Mapping Word Senses of Middle Ancient Chinese to WordNet

Application of TextRank Algorithm for Credibility Assessment

Integrating Pinyin to Improve Spelling Errors Detection for Chinese Language

Exploratory Study of Relationships among Statement Credibility, Context, and Semantic Similarity

WI 2014 Author Index

WI 2014 Publisher's Information

Finding the Needle in the Haystack: Identifying Business Communities in Internet Traffic

Content Provider	ACM Digital Library
Author	Fetzer, Christof Hiltunen, Matti A. Weigert, Stefan
Abstract	Identifying real-world business communities, e.g., Energy, finance, defense, in Internet traffic is a challenging problem but would be valuable for the construction of better in-trusion detection techniques, for example. Seed-based community detection identifies a community in a graph by iteratively adding the 'closest' vertices to an initial set of seed-vertices which are known to belong to the community. Previous research focused on unambiguous networks, where edges describe a specific intention in a fixed domain (e.g., A 'friend' in a social network) and tightly-knit communities whose members are better connected to each other ('close') than to the rest of the network. However, looking at a complete day of raw Internet traffic, we found that (1) the intend of a communication is ambiguous (e.g., ad-downloads are indistinguishable from web-page downloads) and (2) real-world industries manifest themselves as loosely-coupled communities, i.e., With more edges to non-community members than to community members. We present a new seed-based community detection algorithm that provides higher precision and recall in our setting than the related work. We show that this enables the detection of loosely-knit communities using three sample industries. For instance, our solution detected 111 individual energy companies with only 6 false positives, starting from eight ISOs (Independent System Operators) and RTO (Regional Transmission Operators) in the US.
Starting Page	175
Ending Page	182
Page Count	8
File Format	PDF
ISBN	9781479941438
DOI	10.1109/WI-IAT.2014.31
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-08-11
Access Restriction	Subscribed
Subject Keyword	Social networks community discovery seed sets clustering
Content Type	Text
Resource Type	Article

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