AHEAD— efficient Algorithms for HArnessing networked Data — is a PRIN 2017 Project ("Programmi di Ricerca Scientifica di Rilevante Interesse Nazionale", that is, Scientific Research Projects of Relevant National Interest) co-funded by MIUR ("Ministero dell'Instruzione, dell'Università e della Ricerca", the Italian Ministry of University and Scientific Research).

AHEAD project officially started August 29, 2019 and will end on October 27, 2023. The scientific coordinator is Prof. Giuseppe F. Italiano.

The project is organized according to the following workparts and tasks.

WP1: Designing new algorithms for network analysis and for visual analytics.

WP1 will address advanced algorithmic and visualization methodologies for massive network analytics, so as to understand and to represent effectively meaningful properties of large-scale networks, which due to their sheer size pose several new and highly non-trivial challenges. This will require designing new algorithms that are capable of extracting and visualizing key structural properties and patterns on large-scale graphs. WP1 tackles those challenges through the following tasks.

• Task 1.1. Algorithmic Paradigms for Graphs and for Visual Analytics.

• Task 1.2. Efficient Algorithms for Large-Scale Graphs.

WP2: Engineering new algorithms for social networks.

WP2 will explore the practical application of the graph analytics and network visualization methodologies developed in WP1 in the specific domain of social networks. We will take an algorithm engineering approach, by designing practical implementations tailored to the structural characteristics of large-scale social networks (e.g., local density, scale free, power law models, small diameter), their dynamically evolving nature, and domain-dependent features (focusing on economic and financial networks). As detailed in the Collaborative Experimental Validation section, we will tune and experimentally evaluate our algorithms on real-world datasets and distributed programming frameworks (e.g., Apache Spark) targeted at large clusters of commodity machines, assessing their merits against state-of-the-art solutions. To foster adoption of our techniques, all the devised algorithms will be made available from the project website as open-source software, equipped with user manuals and documentation, description of the input and output formats, and available benchmark data. More specifically, WP2 will be organized into the following tasks.

• Task 2.1. Algorithm Engineering for Structural and Visual Network Analytics.

• Task 2.2. Algorithm Engineering for Streamed and Dynamically Evolving Networks.