Co-authorship Networks in Left-Behind Children Research in China: A Social Network Analysis

Rural–urban migration in China has led to significant demographic changes, resulting in many children remaining in their hometowns while their parents seek employment opportunities in urban areas. This dynamic has produced a notable group called “left-behind children” (LBC). The term “left-behind children” typically refers to children who stay in rural areas while one or both of their parents migrate to urban regions for employment (Gao et al., 2010). The issue of LBC in mainland China is a complex and multifaceted problem that spans various dimensions, including social, economic, legal, and health-related aspects (Chen et al., 2020; Fellmeth et al., 2018; Hu et al., 2018). These children face significant challenges that have far-reaching consequences for their wellbeing, development, and societal impact (Nguyen et al., 2006). Recognizing the complexity of this issue, the scholarly community has increasingly adopted a collaborative and interdisciplinary approach to effectively address the challenges faced by this vulnerable population (Ge et al., 2019).

The shift toward team science is evident from the growing number of co-authored publications across various disciplines, reflecting the increasing recognition of the benefits of collaboration in tackling complex research problems (Hall et al., 2018). This trend is particularly notable in LBC research, with a growing number of authors participating in recent studies (Luan et al., 2024). Despite the increasing prevalence of collaborative research in this field, the specific nature and structure of research networks focused on LBC have yet to be extensively examined. Understanding the architecture of these networks is crucial for optimizing the efficiency and effectiveness of collaborative efforts (Patel et al., 2019).

Analyzing the structure of research networks enables the identification of critical leverage points that can enhance their strength and productivity, leading to a more comprehensive and impactful body of research. One primary benefit is the targeted allocation of resources (Fagan et al., 2018). By identifying areas where collaborations are thriving or lacking, resources such as funding, expertise, and infrastructure can be strategically directed to support research in underrepresented or underdeveloped areas (Tijssen & Winnink, 2022). This approach ensures a more comprehensive and balanced understanding of the various aspects of the issue of LBC. Furthermore, understanding research network architecture can prevent overlapping efforts and foster a more efficient research process (Morel et al., 2009). By identifying existing collaborations and research clusters, researchers can focus on complementary or novel aspects of the problem, saving time and resources while contributing to a more diverse body of research (Cummings & Kiesler, 2005; Feng & Kirkley, 2020).

Analyzing research networks also facilitates increased coordination and collaboration among researchers (Cummings & Kiesler, 2005). Identifying key players, central hubs, and potential bridges within the network allows researchers to connect with colleagues with complementary expertise or resources, forming more cohesive and interdisciplinary teams (Ahmed et al., 2018; Giurca & Metz, 2018). This is essential for addressing the complex and multifaceted nature of the LBC issue. Moreover, understanding the structure of research networks helps identify gaps in collaboration and areas where new connections can be forged (Wang et al., 2013). By pinpointing where links are missing or weak, efforts can be made to bridge these gaps and create a more interconnected and robust research community, fostering the exchange of ideas, methodologies, and findings across disciplinary boundaries (Li et al., 2013; Liu & Xia, 2015). This ultimately contributes to a more holistic understanding of the problem and the development of more effective interventions.

While prior research in social work has reported on trends of co-authorship, these studies have primarily focused on counting co-authors over time (e.g., Luan et al., 2024; Victor et al., 2017). Outside social work, a significant body of research has investigated network collaborations and their impact on scholarly productivity and influence (e.g., Li et al., 2013; Seibert et al., 2017). However, to our knowledge, only two studies in social work have specifically examined the relationship between social work collaboration networks and citation counts (Woehle, 2012, 2016). Building upon this foundational work, the current study aims to advance our understanding of collaboration networks within the subfield of left-behind children research. In particular, this study is oriented around the following research objectives:

To map and analyze the structure of research collaboration networks in the field of LBC in mainland China, identifying key authors and universities, central hubs, and potential bridges within the network;

Method

The current study employs social network analysis (SNA) to investigate the research collaboration networks formed by co-authorship patterns among authors studying LBC in mainland China. By focusing on the relationships between authors, this approach provides a powerful tool for examining the structure and dynamics of the research community. We briefly overview network analysis and then break down our study's methodology into four key steps: data preparation, descriptive analysis, centrality analysis, and community detection. Each of these steps will be described in detail in the following sections.

Data Preparation

The current study derives its data from a specialized database created by Luan et al. (2024), which collates research on LBC from peer-reviewed sources. This database includes articles in both Chinese and English. However, our analysis is confined to the English language articles listed in the Web of Science core collection. This decision is driven by technical constraints: the metadata for the Chinese articles did not permit precise matching of authors to their institutional affiliations, and difficulties in accurately translating Chinese names prevented their integration with the English dataset.

While this limitation precludes a broader linguistic scope, focusing on English data presents a distinct advantage. It ensures the utilization of a consistent, reliable dataset, which is crucial for the robust analysis of research collaboration networks. The exclusive analysis of English articles enables a more straightforward examination of these networks within the global academic community. This approach aligns with the prevalent use of English as the lingua franca of international scholarly communication, facilitating wider dissemination and comprehension of the research findings. Thus, the focus on English language articles not only addresses logistical challenges but also enhances the study's relevance and impact within the international research community.

The database was curated through a comprehensive search process, followed by a manual review, and includes an extensive collection of article metadata such as article titles, journal titles, publication years, author names, and author affiliations. Although the database includes literature dating back to 2008, we limit our analyses to 2017 to 2023. This temporal scope was chosen to provide a current snapshot of the research networks and to align with Luan et al.’s (2024) findings, which reported a phase transition in the research during this period, characterized by substantial growth in English language publications and a decline in Chinese literature.

To prepare the data for analysis, we developed a Python script that uniquely links each author's name to their institutional affiliation. We then manually reviewed and standardized each set of names and institutional affiliations. Recognizing that some authors may have changed institutions over time, we standardized all affiliations to their most current ones, ensuring that each author is uniquely represented in the dataset.

We capped the author count per paper at a maximum of seven, roughly one standard deviation (4.14) above the dataset's average number of authors (2.14). In other words, authors with a serial authorship position greater than seven on a given article are removed. This approach helps mitigate the disproportionate impact of high author counts on network complexity, as the number of unique connections (edges) in a fully connected network increases rapidly with each additional author. The number of such unique connections is calculated using the formula of n!/(2!(n− 2)!), where n represents the number of authors. For instance, a single paper in our dataset contained 19 authors. Without restricting the number of authors, this article would have introduced 171 relationships, skewing the network toward a higher density. This extreme case illustrates how large author groups can distort network analysis, leading to overestimating connectivity and collaboration intensity. By imposing a cap, the analysis remains robust against such distortions, providing a more accurate representation of collaboration patterns.

Analytic Strategy

Our analysis consists of two primary sections. Initially, we conduct a macrolevel analysis to describe the entire network. Then, we conduct a microlevel analysis, identifying the most connected authors and significant contributors.

Data Management and Analysis

Data was managed using Python. Tableau was used to create visualizations from tabular data. Network visualizations and computations were performed using Gephi (version 0.10). Gephi is an open-source network analysis and visualization software that provides many tools for exploring and understanding complex networks.

We recognize the importance of making our analysis of co-authorship accessible to a broad audience, especially readers who may not be familiar with these methods. To help readers navigate the technical content, we have included a glossary of relevant SNA terms and formulas in Appendix. We recommend referring to this glossary, especially for those less acquainted with network analysis, to follow the analysis and its implications better. We also acknowledge the use of artificial intelligence tools, specifically ChatGPT and Claude, to assist in editing and refining our manuscript. These tools were used for language improvement, grammar checking, and to help clarify our expressions. We affirm that the intellectual content, analysis, and conclusions are our original contributions.

Results

Macrolevel Analysis

Figure 1 illustrates the network of 886 unique authors after data refinement, which involved limiting to a maximum of seven authors per article. This network, comprising 886 unique authors, displayed 1,944 collaborative links. The average number of collaborations per author, known as the network degree, was 4.39. The network's density, the ratio of actual to possible co-authorships, stood at 0.005, indicating that only 0.5% of all possible author connections were established. This points to a sparse network with minimal interaction or collaboration among authors. Additionally, Figure 2 shows that the network is highly fragmented, evidenced by many isolated components. Many authors, 89.39% of the total (N = 792), contributed to only one paper, lacking collaborative ties to other network components.

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Figure 1.

Collaborative author network analysis on research about mainland China's left-behind children by number of articles. Note. Each circle (i.e., node) represents a unique author. Each connection between nodes (i.e., edge) represents a collaboration on an article.

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Figure 2.

Collaborative author network analysis on research about mainland China's left-behind children by country affiliation. Note. The size of the author node represents the number of publications by the author.

Figure 2 displays the global network of author collaborations, with countries differentiated by node color and the authors’ publication counts by node size. The dataset includes seventeen countries, with mainland China dominating contributions at 81.49%, the United States at 11.29%, and England at 2.26%.

The dataset encompasses 257 distinct universities. Figure 3 illustrates the network of author affiliations, with universities represented by different node colors and publication volume by node size. Key universities with the most substantial representation include Central South University (4.29%), Hunan Normal University (3.27%), and Zhejiang University (3.05%). This diagram demonstrates that collaboration is mostly intra-university, with sporadic links to other institutions.

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Figure 3.

Collaborative author network analysis on research about mainland China's left-behind children university affiliation. Note. The size of the author node represents the number of publications by the author.

Figure 4 presents the co-authorship network's giant component, representing the network's largest connected subgroup of authors. The giant component is crucial to network analysis because it highlights the research community's most interconnected and cohesive portion, where knowledge dissemination and collaborative relationships are most prominent. The giant component comprises 100 authors affiliated with 28 universities across three countries (China, N = 78; the United States, N = 21; England, N = 1), collectively contributing to 156 unique publications. A total of 336 collaborative relationships exist within this component.

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Figure 4.

Largest network component on research involving Mainland China's left-behind children by university affiliation.

This component is extracted from the larger network but is presented using a different layout to reveal its chain-like structure between two authorship groups. These groups are loosely connected by a few publications that act as bridges between them. This bridging phenomenon suggests that while the two authorship groups have internal collaborations and research focus, specific publications establish a connection, facilitating the flow of knowledge and ideas across the groups.

Microlevel Analysis

In the next part of the analysis, we shift our focus from a macrolevel to a microlevel analysis. We first examine the network associations of highly connected authors and then the networks of significant contributors. Throughout all the analyses, we refer to the component ID as a unique identifier of each unique component in the network map. The ID numbers are arbitrary, so no inference should be made based on the actual number.

Connected Authors

In our initial analysis, we examine the collaboration patterns among authors within the largest connected cluster of the network (i.e., the giant component). Table 1 presents the top 15 authors, ranked by their weighted degree of collaboration. These authors form a robust collaborative network, primarily spanning four Chinese institutions (Anhui Medical University, Zhejiang Normal University, Shaanxi Normal University, and Nanjing University) and one U.S. institution (Stanford University). Ten of the 15 institutions are represented by Zhejiang Normal University and Anhui Medical University. While there's a notable correlation between an author's weighted degree and publication count (R²= 0.66), disparities in connectivity among these leading contributors are apparent. The betweenness centrality measure represents an author's role as a bridge within the network, indicating the frequency an author connects different clusters by lying on the shortest paths between them. In this network, values ranged from 1 to 2,688, suggesting a wide disparity in the extent to which authors facilitate the flow of information and collaboration across the network. High values indicate authors who connect disparate groups, enhancing the network's cohesion and information dissemination.

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Table 1.

Most Connected Authors Inside the Giant Component of Authors of Articles on Mainland China's Left-Behind Children.

Author	Country	University	Component ID	Rank	Weighted degree	Degree	Publication count	Between centrality
Xie, Ruibo	China	Zhejiang Normal University	11	1	38	21	8	318
Wu, Wei	China	Anhui Medical University	11	1	38	21	3	893
Li, Weijian	China	Zhejiang Normal University	11	2	37	21	8	318
Rozelle, Scott	USA	Stanford University	11	3	35	31	6	2688
Ding, Wan	China	Zhejiang Normal University	11	4	33	19	7	274
Shi, Yaojiang	China	Shaanxi Normal University	11	5	32	24	6	355
Wang, Die	China	Zhejiang Normal University	11	6	24	17	3	2545
Chang, Fang	China	Shaanxi Normal University	11	7	21	17	4	274
Kenny, Kaleigh	USA	Stanford University	11	8	17	15	3	179
Kayani, Sumaira	China	Zhejiang Normal University	11	9	16	9	3	2
Wang, Lingling	China	Anhui Medical University	11	10	14	7	3	1
Qu, Guangbo	China	Anhui Medical University	11	10	14	7	3	1
Tang, Xue	China	Anhui Medical University	11	10	14	7	3	1
Jiang, Min	China	Zhejiang Normal University	11	11	12	8	2	1
Wang, Lei	China	Nanjing University	11	11	12	12	2	2590

Table 2 displays the top 15 most connected authors located outside the largest connected cluster of the network. These authors are affiliated with Chinese institutions, representing seven different universities. Notably, all authors from Zhejiang University belong to the same smaller cluster, indicating a strong pattern of collaboration within this university. Similarly, authors from Central South University and Peking University are also each confined to their respective distinct clusters, highlighting a significant level of intra-university collaboration. The range of value on the betweenness centrality measure was considerably less than what was observed in the giant component. In this network, the values ranged from 4 to 415.

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Table 2.

Most Connected Authors Outside the Giant Component of Authors of Articles on Mainland China's Left-Behind Children.

Author	Country	University	Component ID	Rank	Weighted degree	Degree	Publication count	Between centrality
Lu, Jingjing	China	Zhejiang University	13	1	30	19	7	146
Wang, Feng	China	Zhejiang University	13	1	30	19	8	313
Zhou, Xudong	China	Zhejiang University	13	2	28	19	6	258
Li, Lu	China	Zhejiang University	13	3	25	18	5	300
Zou, Jiaojiao	China	Central South University	17	4	21	15	4	39
Wang, Xiaoli	China	Peking University	84	5	18	11	3	21
Zhang, Jingxu	China	Peking University	84	5	18	11	3	21
Hu, Hongwei	China	Renmin University China	18	5	18	11	4	9
Jiang, Haochen	China	North China Electric Power University	18	5	18	11	4	9
Shi, Huifeng	China	Peking University	84	6	17	12	2	65
Yuan, Tong	China	Central South University	17	7	16	10	3	4
Xu, Xiao	China	Changzhou University	58	8	15	10	2	24
Jiang, Haixia	China	Peking University	18	8	15	10	2	5
Ji, Meimei	China	Central South University	17	8	15	10	3	4
Lin, Danhua	China	Beijing Normal University	4	8	15	14	4	415

Significant Contributors

Table 3 showcases the leading researchers in the study of LBC, with their publication counts varying between four and eight. Among these top contributors, 13 are affiliated with Chinese universities, with Zhejiang Normal University and Zhejiang University having the most representatives. Despite their significant contributions to the field, there is a notable diversity in their centrality measures, indicating a wide range in their connectivity within the network, even amidst high volumes of scholarly output.

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Table 3.

Network Measures Among the Largest Contributors to Research on Mainland China's Left-Behind Children.

Author	Country	University	Component ID	Rank	Weighted degree	Degree	Publication count	Between centrality
Xie, Ruibo	China	Zhejiang Normal University	11	1	38	21	8	318
Li, Weijian	China	Zhejiang Normal University	11	1	37	21	8	318
Wang, Feng	China	Zhejiang University	13	1	30	19	8	313
Ding, Wan	China	Zhejiang Normal University	11	2	33	19	7	274
Lu, Jingjing	China	Zhejiang University	13	2	30	19	7	146
Rozelle, Scott	USA	Stanford University	11	3	35	31	6	2688
Shi, Yaojiang	China	Shaanxi Normal University	11	3	32	24	6	355
Zhou, Xudong	China	Zhejiang University	13	3	28	19	6	258
Li, Lu	China	Zhejiang University	13	4	25	18	5	300
Chen, Xiaojin	USA	Tulane University	4	4	10	7	5	150
Chang, Fang	China	Shaanxi Normal University	11	5	21	17	4	274
Hu, Hongwei	China	Renmin University China	18	5	18	11	4	9
Jiang, Haochen	China	North China Elect Power University	18	5	18	11	4	9
Lin, Danhua	China	Beijing Normal University	4	5	15	14	4	415
Zou, Jiaojiao	China	Central South University	17	5	21	15	4	39

Discussion and Applications to Practice

The present study aimed to map and analyze the structure of English research collaboration networks in the field of LBC in mainland China, identifying key authors, universities, and collaboration patterns. By employing SNA techniques, we sought to gain insights into the dissemination and exchange of knowledge within this research community, ultimately informing strategies to enhance research efficiency and impact.

Our findings underscore the importance of analyzing research collaboration networks in LBC's research. By identifying central hubs, potential bridges, and gaps in collaboration, this study contributes to a more comprehensive understanding of the research landscape. The macrolevel analysis revealed a fragmented network structure, with many small and disconnected components since many authors published only one scientific paper. This is a significant problem that deserves further consideration. For social workers, this fragmented structure indicates that greater efforts are needed to foster sustained collaboration and engagement among researchers. The lack of continuous research partnerships may also mean that interventions in practice could lack the necessary evidence base to evolve over time. On the one hand, this phenomenon may be explained by many early-career scholars, but they do not continue to pursue this line of research in their subsequent careers. This could be due to a lack of funding opportunities, limited institutional support, or a shift in research interests. Additionally, some researchers may contribute to the field through a one-time collaboration with more established scholars, leading to a single co-authored publication. On other hand, this phenomenon may be derived from the university funding policy in mainland China, regarding the quantity and quality of international publications, sequence of authorship, and the form in which acknowledgement should be made add additional pressure on scholars (Jiang et al., 2017).

However, the prevalence of single-paper authors in the network may also indicate a lack of sustained engagement with the issue of LBC, which could hinder the development of a cohesive and cumulative body of knowledge. Without a core group of dedicated researchers consistently working on this issue, building upon previous findings, identifying new research directions, and translating research into effective policies and interventions becomes more challenging. For social work practice, this implies a need for increased institutional and collaborative support, encouraging long-term research that can be translated into sustained interventions for LBC populations.

At the microlevel, analyzing individual authors’ influence and connectivity within the network provides valuable insights into the roles of key players in facilitating knowledge exchange and driving research progress. By identifying highly connected authors and their roles as bridges between research clusters, we can gain deeper insights into the mechanisms that facilitate collaboration and communication across institutional and disciplinary boundaries. Understanding these bridging positions helps illuminate how knowledge is disseminated and integrated among different groups, thereby enhancing the collective efficacy of the research community. For social workers, understanding these dynamics allows them to identify potential collaborators for evidence-based interventions and policy development.

Our findings reveal that the most connected and influential authors are affiliated with Zhejiang Normal University, Zhejiang University, and Anhui Medical University. These universities are crucial in shaping the research landscape and facilitating knowledge exchange. These institutions’ high connectivity and influence can be attributed to several factors. First, they have a strong research focus on issues related to LBC, with dedicated research teams and resources allocated to this area. Second, they showed good performance in raising university rankings in recent years based on the Shanghai Ranking system (ShanghaiRanking Consultancy, 2024), which means a strong correlation with research productivity (Li et al., 2011), and have established collaborative relationships with other institutions and researchers within China and internationally, enabling them to leverage a wide range of expertise and resources.

Moreover, the geographical proximity of these universities further enhances their potential for collaboration. Zhejiang Normal University and Zhejiang University are in Zhejiang Province, while Anhui Medical University is in the neighboring Anhui Province. This proximity allows for easier communication, face-to-face meetings, resource sharing, and facilities, which can greatly facilitate collaborative research efforts. From a social work practice perspective, the ability of these universities to bridge various research clusters indicates a stronger potential to influence practical interventions for LBC through knowledge transfer and interdisciplinary collaboration.

The findings of this study have several practical implications for social work practice. By identifying central hubs, potential bridges, and gaps in collaboration, this study provides social workers with actionable insights into how knowledge on LBC is generated and disseminated. Social workers can use these insights to foster interdisciplinary partnerships, ensuring that social work expertise is central to developing support systems for LBC. Practitioners can collaborate with leading researchers and institutions to integrate social work practice into the broader discourse on LBC, thereby developing more comprehensive interventions.

To enhance these interdisciplinary collaborations, social work researchers should focus on strategies that optimize resource allocation and strengthen networks of scholars working on LBC issues. One actionable strategy for social work institutions is to support researchers and institutions that serve as bridges between research clusters, such as Zhejiang Normal University, Zhejiang University, and Anhui Medical University. By fostering partnerships with these influential institutions, social workers can amplify the impact of research through practice-oriented interventions. Policymakers can encourage the formation of interdisciplinary research teams and facilitate the flow of knowledge and ideas across the network by providing targeted funding and support to these institutions.

Moreover, funding agencies should prioritize projects that involve collaboration between researchers from different disciplines and institutions. This can be achieved by establishing specific grant programs requiring interdisciplinary partnerships or by incentivizing researchers to engage in collaborative work. In practice, social workers could participate in these grants to ensure their interventions are informed by and contribute to cutting-edge research.

In addition to funding, our findings highlight the importance of creating opportunities for social work researchers to connect and share knowledge. Several strategies can be employed to achieve this goal. First, establishing formal research networks or consortia can provide a platform for researchers to connect, share knowledge, and collaborate on projects. Second, organizing conferences, workshops, and seminars that bring together researchers from diverse backgrounds can facilitate the exchange of ideas and methodologies, leading to new collaborations. Finally, creating online resources, such as databases and discussion forums, can enable researchers to access and contribute to a shared knowledge base, promoting a more cohesive research community.

By implementing these strategies, social work research community can foster a more interconnected and robust research community, ultimately enhancing the efficiency and impact of LBC research in China. The insights provided by our network analysis demonstrate the value of this approach in tackling complex social issues within the broader field of social work research.

The current study significantly contributes to the broader social work research field in three key areas. First, it demonstrates the value of network analysis in addressing complex social issues, such as the challenges LBC face in China. By examining the structure and dynamics of research collaboration networks, this study provides a comprehensive understanding of the research landscape, identifying key players, collaboration patterns, and gaps in knowledge. The use of SNA offers social work practitioners a powerful tool for mapping and understanding the systems that affect vulnerable populations, leading to more effective solutions.

Second, this study expands the application of network analysis techniques in social work research. While network analysis has been applied in various fields, its use in social work research has been limited. By demonstrating the value of network analysis in understanding research collaboration networks, this study encourages social work researchers to adopt these techniques to investigate a wide range of social issues, such as social support networks, service delivery networks, and policy networks. These insights provide a new methodological framework for social workers to explore how networks of support and collaboration can be optimized for vulnerable populations, offering a more nuanced approach to addressing complex social challenges like those faced by LBC.

Finally, the findings of this study inform the development of more effective and efficient research strategies in social work. Understanding the structure and dynamics of research collaboration networks enables social work researchers to identify opportunities for interdisciplinary collaboration, optimize resource allocation, and develop targeted strategies to address knowledge gaps. The insights gained from network analysis can guide the formation of research teams, the prioritization of research questions, and the dissemination of findings to maximize research impact. By leveraging the power of collaboration and strategic resource allocation, social work researchers can develop more efficient and effective approaches to addressing complex social issues and improving the lives of individuals and communities, particularly in vulnerable populations such as LBC.

While this study provides valuable insights into the research collaboration networks in the field of LBC in China, it is important to acknowledge its limitations and identify future directions for research. One limitation of this study is excluding Chinese language articles from the analysis. Due to differences in metadata structure and challenges associated with translating names, this study focused solely on English language articles indexed in the Web of Science core collection. However, given the significant body of research on LBC published in Chinese language journals, excluding these articles may impact the overall network structure and identify key authors and institutions. Future research should address this limitation by developing methods to effectively integrate Chinese language articles into the network analysis, providing a more comprehensive understanding of the research landscape.

In addition to the aforementioned limitations, it is important to note the structural challenges in analyzing social work-specific data within Chinese academia. Social work programs in China are frequently housed within larger sociology departments or public administration schools, rather than being established as independent schools of social work, as is common in other countries such as the United States. As a result, it was not feasible to isolate specific results pertaining exclusively to social work scholars or departments. Future research may benefit from more targeted data collection efforts that explicitly focus on social work scholars, enabling a deeper exploration of their unique contributions within the field of LBC research.

Another limitation of this study is its cross-sectional design, which simultaneously provides a snapshot of the research collaboration networks. To gain a deeper understanding of the dynamics of these networks, future research should explore their evolution over time. By conducting longitudinal network analyses, researchers can identify trends in collaboration patterns, track the emergence of new research clusters, and assess the impact of key events or policy changes on the research community. This temporal perspective can provide valuable insights into the factors that shape research collaboration networks and inform strategies to promote their growth and sustainability.

While this study employed a range of network analysis techniques, including centrality measures and community detection, there are opportunities to further enrich our understanding of research collaboration networks by incorporating additional techniques and data sources. For example, future research could employ text-mining techniques to analyze the content of research articles, identifying key themes and topics that shape collaboration patterns. Additionally, incorporating data on research funding, co-citation networks, and social media interactions could provide a more comprehensive understanding of the factors influencing research collaboration and knowledge dissemination. Researchers can develop a more nuanced and multifaceted understanding of research collaboration networks by leveraging these additional techniques and data sources.

This study used SNA techniques to map and analyze the structure of research collaboration networks in the field of LBC in China, highlighting the fragmented nature of the research community and identifying key authors, institutions, and opportunities for collaboration. The implications of these findings are significant, providing a roadmap for fostering a more interconnected and robust research community, informing the strategic allocation of resources, and emphasizing the importance of collaboration and interdisciplinary approaches in addressing complex social issues. Beyond its implications for LBC research, this study highlights the potential of network analysis to inform research strategies and optimize impact in social work research. By embracing network analysis techniques and leveraging the insights they provide, social work researchers can enhance their ability to develop effective and efficient strategies to address complex social issues and improve the lives of vulnerable populations.