Conference Agenda

How do interiorized learning patterns from art school shape the ways artists work? The digital turn constitutes a process of institutionalization of artistic practices. Previous studies show that art studios are spaces for cultural and social capital formation. Moreover, current practices are linked to learning conditions and previously embodied, distributed and extended cognitive experiences. Digital and analog tools are key epistemic objects that reproduce patterns of artistic gossip among artists and beyond the studio walls. We claim that video-aided ethnography shows how artistic networking shapes the cognitive process of artistic practice. The sample consists of 45 interviewed artists from different generations and four visual disciplines, with more than 10 years of professional experience, balanced by age and gender. The mixed methods design is a video aided ethnography with interviews, observation, participatory photography, video elicitation, SNA, and focus groups. Qualitative data analysis includes grounded theory, thematic analysis, conversational analysis, multimodal analysis with ELAN and quantitative analysis of egonetworks with Egonet/Ucinet. Centrality and compositional measures formalize the accumulation of successful relationships. The visualization of the network is a useful tool to produce more specific discursive data from the subjects, as well as an objective contrast to their expectations on how they socialize among their colleagues and friends. At the same time, artistic gossip from ethnographic observation and interviews contextualizes the networking patterns. Structural factors permeate the micro-level so that networking becomes a key component of the creative process.

During and up to the present moment in the academic formation journey, I have recognized myself as a relational sociologist, currently interested in coordination of collective action engaged in climate change. I have designed the doctoral research as a relational ethnography to investigate how the unfolding of collaborative relationships among collective actors shapes the identization process during a wave of mobilization. Between 2019 and 2024 the research design followed a parallel mixed-methods approach on the case of Fridays for Future in Italy. Participant observation and thick descriptions of meetings have led to the investigation of the decision-making procedures shaped by ties’ dynamics. Two survey’s rounds have produced longitudinal and multi-layer whole and ego-network data, further enriched by in-depth interviews capturing alter-alter ties, which have allowed for an analysis of the roles within the social structure. Notably, in-depth interviews have facilitated the emergence of the grassroots collective actors’ narratives which, through within-case and cross-cases coding of relational mechanisms, have reconstruct the pathways of modes of coordination traversed by the grassroots collective actors. The paper aims to unfold the choices, difficulties and solutions faced during the research process, engaging with studies in which network is both a theoretical concept and an analytical tool. Thus, the themes explored concern: the fluidity of the boundaries delineated by co-attendance ties; the relational ingredients chosen, found, and re-evaluated as relevant for the identization process; the relational mechanisms that shape the collective actors’ pathways; and finally, the dynamics of trust relationships within the research process itself.

In recent years, the literature has proposed different studies that combine the analysis of node-level attributes alongside topological information of the network. These proposals range from hierarchical clustering algorithms including relational constraints to communities in the context of Subgroup Discovery, and data-driven probabilistic methods on multilayer networks. While these methods have been proposed to integrate structural and attribute-based information, achieving a balanced and coherent representation remains challenging. In this work, we apply DISTATIS, a three-way multidimensional scaling technique, to jointly analyze network topology and node attributes. Through simulations on networks with different attribute types, our results demonstrate that DISTATIS effectively captures the coherence between the attributes (qualitative and quantitative) and network structure. This approach offers a valuable tool for extracting complex relationships in real-world networks where both structural and attribute-driven factors are crucial.

Clusters or communities can provide a coarse-grained description of complex systems at multiple scales, but their detection remains challenging in practice. Community detection methods often define communities as dense subgraphs, or subgraphs with few connections in-between, via concepts such as the cut, conductance, or modularity. Here we consider another perspective built on the notion of local dominance, where low-degree nodes are assigned to the basin of influence of high-degree nodes, and design an efficient algorithm based on local information. Local dominance gives rises to community centers, and uncovers local hierarchies in the network. Community centers have a larger degree than their neighbors and are sufficiently distant from other centers. The strength of our framework is demonstrated on synthesized and empirical networks with ground-truth community labels. The notion of local dominance and the associated asymmetric relations between nodes are not restricted to community detection, and can be utilised in clustering problems, as we illustrate on networks derived from vector data.

The increasing involvement of academics and intellectuals in the Italian public debate, often on topics that go beyond their original expertise, raises questions about the role of expert knowledge and its interaction with the media field. Building on this premise, we propose an analysis of the relational and content dynamics shaping public debate of experts and intellectuals in their digital interactions.

This study adopts a mixed-methods approach that integrates Social Network Analysis (SNA) with statistical text analysis techniques. Specifically, we will examine the network of interactions among public figures in the Italian context on Social Network X, where connections — defined through mentions, replies, and retweets — will be labeled according to thematic categories extracted via cluster analysis using the Reinert method, applied to the corpus of posts published by the authors.

In our view, this analytical strategy allows us to explore the relationship between network structure and the specialization or transversality of discourses, providing a framework to assess whether digital public debate tends to fragment into thematically defined communities or, conversely, whether more hybrid and interconnected configurations emerge.

From a methodological perspective, this study addresses key challenges in network construction, including the thematic characterization of ties, the management of intersections between multiple discursive categories, and the definition of criteria for measuring the thematic specialization or transversality of specific relational configurations. The ultimate goal is to determine whether the structure of digital interactions reinforces the polarization of public debate or, alternatively, facilitates more fluid and interconnected dynamics.