TY - GEN
T1 - Validating Vector-Label Propagation for Graph Embedding
AU - Bellandi, Valerio
AU - Damiani, Ernesto
AU - Ghirimoldi, Valerio
AU - Maghool, Samira
AU - Negri, Fedra
N1 - Funding Information:
In a collaborative study funded by the University of Milan, involving the Department of Social and Political Sciences and the Department of Computer Science, the Populite project has been launched. The aim is to study the behavioral patterns of Italian politicians on social media. A key aspect of this study is to depict the communities and sub-communities that the communication flow and the social network among Italian politicians on social media create. By studying the inter and intra-cohesion of these communities multiple interesting questions can be answered. Which are the political groups that interact the most, which ones are partitioned into sub-communities (i.e., intra-party factions), and to which other political groups these sub-communities are connected? Individual links can also be analyzed. Is there reciprocity between parliamentarians? Which ones are similar, based on their “neighborhood”?
Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Structural network analysis retrieves the holistic patterns of interactions among network instances. Due to the unprecedented growth of data availability, it is time to take advantage of Machine Learning to integrate the outcome of the structural analysis with better predictions on the upcoming states of large networks. Concerning the existing challenges of adopting methods embracing multi-dimensional, multi-task, transparent representations within incremental procedures, in our recent study, we proposed the AVPRA algorithm. It works as an embedder of both the network structure and domain-specific features making the aforementioned challenges feasible to address. In this paper, we elaborate on the validation of AVPRA by adopting it in multiple downstream Machine Learning tasks on the Twitter network of the Italian Parliament. Comparing the outcome with state-of-the-art algorithms of graph embedding, the capability of AVPRA in retaining either network structure properties or domain-specific features of the nodes is promising. In addition, the method is incremental and transparent.
AB - Structural network analysis retrieves the holistic patterns of interactions among network instances. Due to the unprecedented growth of data availability, it is time to take advantage of Machine Learning to integrate the outcome of the structural analysis with better predictions on the upcoming states of large networks. Concerning the existing challenges of adopting methods embracing multi-dimensional, multi-task, transparent representations within incremental procedures, in our recent study, we proposed the AVPRA algorithm. It works as an embedder of both the network structure and domain-specific features making the aforementioned challenges feasible to address. In this paper, we elaborate on the validation of AVPRA by adopting it in multiple downstream Machine Learning tasks on the Twitter network of the Italian Parliament. Comparing the outcome with state-of-the-art algorithms of graph embedding, the capability of AVPRA in retaining either network structure properties or domain-specific features of the nodes is promising. In addition, the method is incremental and transparent.
KW - Graph embedding
KW - Social network analysis
KW - Vector-label propagation
UR - http://www.scopus.com/inward/record.url?scp=85140446017&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-17834-4_15
DO - 10.1007/978-3-031-17834-4_15
M3 - Conference contribution
AN - SCOPUS:85140446017
SN - 9783031178337
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 259
EP - 276
BT - Cooperative Information Systems - 28th International Conference, CoopIS 2022, Proceedings
A2 - Sellami, Mohamed
A2 - Gaaloul, Walid
A2 - Ceravolo, Paolo
A2 - Reijers, Hajo A.
A2 - Panetto, Hervé
PB - Springer Science and Business Media Deutschland GmbH
T2 - 28th International Conference on Cooperative Information Systems, CoopIS 2022
Y2 - 4 October 2022 through 7 October 2022
ER -