Purpose-oriented networks (PONs) are interorganizational networks working collectively to address complex challenges. PONs are often comprised of organizations from different sectors and service domains. Research to date has examined the structure of such networks, but determining effective structures for PONs is complicated as these networks vary in context, purpose, size, etc. In addition, the study of these network structures tends to focus on the whole network structure, assuming a uniform structure despite the diversity of service domains represented in the network. This study is an analysis of the structural subgroups present in PONs guided by the question of whether service domain explains any subgroup clustering. We analyze five different PONs that vary in purpose and stage of network development, but all aiming to connect organizations from different service domains. We employ the Markov clustering function to identify any clustering of subgroups. The algorithm deduces the number of clusters based on the structure of the network by partitioning it into non-overlapping clusters. Unlike more recently developed community detection methods, the Markov clustering method is well suited to small datasets and thus an appropriate method for analyzing PONs. It detects indirect ties or communities that may not be detected through clique-detection algorithms. We analyze the results of the clustering analysis to examine whether we find patterns of subnetwork clustering related to service domain across different networks. We discuss the implications of our findings for advancing research on PON structure and the challenges of connecting multiple service domains.

The present paper examines how large cross-sectoral inter-organizational networks that create public value can be benchmarked from a structural perspective. To this end, we study two publicly funded innovation programs aimed at stimulating collaboration between universities, research institutes, and industry partners. Both programs funded 1800-1900 projects in multiple industries over periods of 6 and 24 years, with projects averaging 4-5 participating organizations. Through overlapping participation in other projects, organizations often form very large system-oriented networks in areas such as Agri & Food, Energy Transition, or Delta Technology. In these networks, information exchange between projects and information flows between organizations take place, which are usually considered to be beneficial for the overall outcome of such policy schemes, especially if network structural features resemble a small-world network structure. Questions that interest us in this context are what their structural characteristics are, and how such networks differ across sectors, over time, and in particular, between the two innovation programs. Would both programs be equally successful in stimulating small-world networks characterized by cross-sectoral collaboration? Using exponential random graph models for multiple networks (ERGM.multi), along with additional approaches (simulation/coefficient comparison), we distinguish generic network patterns from industry/year-specific network characteristics.

Effective collaboration between health care and social service organizations is essential for addressing complex challenges, particularly in cases involving vulnerable populations. While interprofessional collaboration between organizations offers significant advantages, professionals tend to collaborate primarily within their own disciplinary boundaries, leading to segmented organizational network structures. This study examines the collaborative network structure of organizations involved in providing comprehensive multidisciplinary care for individuals with mild intellectual disabilities facing planned or unplanned parenthood in Groningen, the Netherlands.

We collected data on 785 collaborative ties of 142 professionals working in 109 health care and social service organizations participating in a local interorganizational network initiative aimed at enhancing cross-organizational collaboration. Data were gathered at both the professional and organizational levels, allowing for a detailed analysis of the network’s multilevel structure. Our study seeks to understand professionals’ satisfaction with their collaborations on this issue and to model how collaborative reachability, frequency, and satisfaction shape the interorganizational network.

At the organizational level, we employ Exponential Random Graph Models to investigate the micro-processes driving the interorganizational network’s structure. At the professional level, we collected additional data on satisfaction, reachability, and collaboration frequency. Preliminary findings indicate that medical and social care professionals exhibit distinct collaboration preferences, with reachability playing a more significant role in shaping collaborative satisfaction among social care professionals than among medical professionals. The analysis of the interorganizational network structure is ongoing and will be presented at the session.

This paper presents preliminary results from a social network analysis of international and local partnerships in the Sudan humanitarian response, as part of a broader mixed methods research project examining Sudanese crisis leadership and mutual aid.

Using Gephi for network analysis and visualisation, the study maps relationships between key international and local actors in delivering humanitarian response in the current humanitarian crisis in Sudan. Analysis focuses on funding and resource flows, decision-making and autonomy (e.g. degree of control over how resources are used), and gatekeeping functions (who are the key brokers within the network). A key preliminary finding is that that informal and grassroots actors, including emergency response rooms, which are driving the frontline provision of urgent humanitarian aid, are over-reliant on larger ‘gatekeeping’ organisations which receive the bulk of international funding. In foregrounding Sudanese expertise and crisis leadership, the study highlights failures of the international humanitarian system to shift power and resources to ‘local’ actors, a key aim of international humanitarian policy, since 2016.

The study offers a more nuanced understanding of Sudan’s mutual aid and civil society response. By amplifying Sudanese voices and knowledge, this research enhances global discussions on humanitarian leadership and partnerships, advocating for more effective and locally driven crisis response. The paper highlights the unique perspective SNA analysis can bring in complex humanitarian settings, as well as some challenges and experiences of SNA in this context.