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2017 Proceeding- Editors:
- Akhil Arora,
- Shourya Roy,
- Arnab Bhattacharya
We are delighted to present the papers from the 2nd NDA Workshop on Network Data Analytics, which took place on 19th May, 2017 co-located with the ACM SIGMOD conference in Chicago, Illinois, USA.
Networks are prevalent in today's electronic world in a wide variety of domains ranging from engineering to social sciences, life sciences, physical sciences, and so on. Researchers and practitioners have studied networks in multiple ways like defining network metrics, providing theoretical results and examining problems like pattern mining, link prediction, etc. The NDA workshop is a forum for exchanging ideas and methods for mining, querying and learning with real-world networks, developing new common understandings of the problems at hand, sharing of data sets where applicable, and leveraging existing knowledge from different disciplines. The purpose of this workshop is to bring together researchers from academia, industry, and government, to create a forum for discussing recent advances in (large-scale) graph analysis, as well as propose and discuss novel methods and techniques towards addressing domain specific challenges and handling noise in real-world graphs.
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Construction of Structured Heterogeneous Networks from Massive Text Data: Extended Abstract
Network data analytics is important, powerful, and exciting. How big role may network data analytics play in the real world? Much real-world data is unstructured, in the form of natural language text. A grand challenges on big data research is to ...
Repairing Noisy Graphs
Graphs are a flexible way to represent data in a variety of applications, with nodes representing domain-specific entities (e.g., records in record linkage, products and types in an ontology) and edges capturing a variety of relationships between these ...
Graph Mining to Characterize Competition for Employment
In this paper, we discuss a novel application of graph analytics to characterize competition in the workforce. We propose a methodology that relies on finding communities in a graph representing prospective employees (with edges connecting people who ...
Performance Prediction for Graph Queries
Query performance prediction has shown benefits to query optimization and resource allocation for relational databases. Emerging applications are leading to search scenarios where workloads with heterogeneous, structure-less analytical queries are ...
Using Graphical Features To Improve Demographic Prediction From Smart Phone Data
Demographic information such as gender, age, ethnicity, level of education, disabilities, employment, and socio-economic status are important in the area of social science, survey and marketing. But it is difficult to obtain the demographic information ...
SCAN-XP: Parallel Structural Graph Clustering Algorithm on Intel Xeon Phi Coprocessors
The structural graph clustering method SCAN, proposed by Xu et al., is successfully used in many applications because it not only detects densely connected nodes as clusters but also extracts sparsely connected nodes as hubs or outliers. However, it is ...
Web and Social Media Analytics towards Enhancing Urban Transportations: A Case for Bangalore
Cities today are typically plagued by multiple issues such as âĂŞ traffic jams, garbage, transit overload, public safety, drainage etc. Citizens today tend to discuss these issues in public forums, social media, web blogs, in a widespread manner. Given ...
- Proceedings of the 2nd International Workshop on Network Data Analytics
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Acceptance Rates
| Year | Submitted | Accepted | Rate |
|---|---|---|---|
| NDA '16 | 8 | 4 | 50% |
| Overall | 8 | 4 | 50% |