The Ubiquitous Internet Unit of IIT-CNR (Pisa, Italy) is scouting for talented researchers, post-doc level. We are collecting expressions of interest for the following research topic:Signed graphs for online social networks: data-driven characterization, temporal dynamics, applicationsThe position is open atpostdoctoral level, with the research direction adaptable to the expertise and interests of the applicant. Candidates with strong analytical and computational skills, as well as an interest insocial network analysis, AI/ML, and data-driven research, are encouraged to apply.Candidate profile:
PhD inComputer Science, Physics (complex networks), or Mathematics.
Strong programming skills fordata collection, processing, and model training.
Experience or interest ininterdisciplinary research, such as computational social science.IIT-CNR will open a formal call for the position. The scouting process is intended to advertise the above topic in view of the call.On the research topic
Since Granovetter’s seminal work on the strength of social ties, we have understood that social relationships are shaped by multiple factors, including frequency of interaction, trust, and emotional connection. Most research on online social networks has focused on interaction frequency, as it is the easiest metric to compute and quantify. However, recent studies have shifted toward more qualitative aspects of relationships, moving beyond mere interaction counts to understanding the nature of social connections.
One of the most widely used ways to encode the quality of relationships is by assigning a positive or negative sign to them. Positive relationships often represent trust, homophily, or mutual support. Negative relationships may indicate conflict, distrust, or rivalry.
Everyday experience shows that information does not flow equally through positive and negative ties—for instance, people may avoid sharing personal details with those they do not trust. This fundamentally challenges traditional models of social communities, information diffusion, and network structure. When social networks are represented as graphs, these signs are attached to the links between nodes. While emerging properties like information diffusion, community formation, polarisation, have been largely investigated by modeling social networks asunsignedgraphs, the effect of the polarity (positive or negative) of ties on these processes is still largely unexplored.
Despite the importance ofsigned relationships, a major limitation in the field is thelack of reliable ground truth datasets. The few existing signed datasets are often outdated and domain-specific (e.g.,product ratings, online reviews), making them unsuitable for studyinggeneral-purpose notions of positive and negative relationships in diverse social settings.This research position aims tofill this gapby designing and deployingnew methodologies to
collect ground truth signed graphs
use it totrain AI/ML modelsandimprove social network analysis.Possible approaches include:
Conductingsurveys and experimentsto gather data, either through direct administration or via crowdsourcing platforms likeMechanical Turk.
Leveraginglarge language models (LLMs) such as ChatGPTto infer relationship signs at scale.
Developingnovel, non-ML-based methodsforinferring signed relationshipsfrom collected social interactions data.
Applying the collected datasets and derived models to improvedownstream tasks, such ascommunity detection, information diffusion modeling, and polarization analysis.Funding and partnershipsThe activities of this topic will be supported by:
: Extended Partnership on Artificial Intelligence (funded by the National Recovery and Resilience Plan (NRRP), European Union - NextGenerationEU)
: National Research Centre for High-Performance Computing, Big Data and Quantum ComputingFurther information: c.boldrini@iit.cnr.it