Using Colored Motifs to Characterize Relationship Patterns of Project Networks in Transportation Sectors
Xiaowei Feng et al.
Abstract
This study introduces a colored network motif approach to systematically analyze micro-level collaboration patterns in PPP project networks from China's transportation sector. By distinguishing node types (e.g., contractors, investors) and tie strengths (e.g., strong vs. weak collaboration), the analysis uncovers statistically significant triadic motifs that reveal structural dominance and evolutionary features. Using a longitudinal, multi-project dataset (2014–2021) and degree-preserving randomized network benchmarks, the study ensures robust identification of overrepresented local structures. Key findings show that (1) in terms of heterogeneous-node local relationship patterns, construction contractors emerge as dominant players, with fully connected triads and structural hole triads wherein dyads are different types of organizations identified as motifs; (2) concerning heterogeneous-edge patterns, even though weak-tie subgraphs form the basic path of the networks, stable strong-tie subgraphs, representing collaboration alliances, become increasingly prevalent among organizations. This study contributes in three key ways. First, it advances project network analysis by shifting the analytical lens from macro-level structures to micro-relational patterns, enabling the identification of recurrent subgraph configurations among PPP actors. Second, it operationalizes colored motif detection to empirically capture both node- and edge-level heterogeneity in PPP networks. Third, it offers new theoretical and practical insights into the evolution of collaboration structures, showing how motifs such as structural holes reflect underlying governance logics.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.