| 1. Domain | 1.1 Scope of the Domain | Boundaries | The range of phenomena the science includes and excludes. | Examines how social ties, relational structures, patterns of connection, and flows of information, influence, support, and resources shape individual and collective behavior. Excludes strictly psychological dyadic processes unless embedded in network relations, and excludes macro-structures not reducible to relational patterns. |
| | Scale | The spatial, temporal, or organizational level at which the science operates (e.g., quantum, cellular, social, cosmic). | Operates at meso- to micro-macro linkage scales: dyads, triads, ego networks, whole networks, inter-organizational networks, temporal interaction networks, and dynamic relational systems. |
| 1.2 Ontological Commitments | Entities | The kinds of things assumed to exist within the domain (particles, organisms, agents, fields, etc.). | Actors (individuals, groups, organizations), ties (strong/weak), structural positions, relational roles, network edges, multiplex ties, flows of information, influence, support, and resources. |
| | Properties | The fundamental attributes these entities possess (mass, charge, genotype, preference, etc.). | Tie strength, reciprocity, centrality, brokerage, cohesion, structural equivalence, multiplexity, clustering, transitivity, homophily, diffusion thresholds, relational stability/volatility. |
| | Categories | The basic ontological types used to classify domain elements (substances, processes, relations, structures). | Ego networks, dyads, triads, clusters, components, cohesive subgroups, brokerage structures, structural holes, multiplex networks, temporal networks, directed/undirected networks. |
| 1.3 State-Variables | Variables | The measurable or definable properties that describe system conditions. | Degree, betweenness, closeness, eigenvector centrality, clustering coefficients, tie frequency, tie stability, relational balance, triadic closure, diffusion states, structural position indices. |
| | Parameterization | How variables encode and represent the system’s state. | Encoded through adjacency matrices, edge lists, weighted graphs, temporal interaction logs, relational coding schemes, centrality vectors, structural equivalence profiles, diffusion curves. |
| 1.4 Admissible Idealizations | Simplifications | Conceptual reductions used to make the domain tractable (point masses, rational agents, perfect gases). | Treating ties as binary; ignoring multiplex layers; assuming stable networks; simplifying tie strength; neglecting unobserved ties; assuming homogeneous influence processes; using static snapshots for dynamic networks. |
| | Validity Conditions | The limits and contexts in which idealizations hold or break down. | Breakdowns in fast-changing networks; hidden ties; multiplex or overlapping relational layers; high-degree missing data; contexts with extreme relational volatility; algorithmic misclassification of ties. |
| 1.5 Domain Assumptions | Structural Assumptions | Background ontological stances such as determinism, continuity, randomness, discreteness. | Assumes relational structures shape behavior; network positions constrain opportunities; ties transmit resources, information, and influence; structural patterns persist long enough to exert effects; homophily and transitivity shape tie formation. |
| | Implicit Commitments | Unstated but necessary assumptions that shape the field’s conceptual structure. | Assumes relational data approximate real ties; actors respond to structural opportunities; patterns are meaningfully measurable; networks can be decomposed into nodes/edges without losing essential relational information. |
| 1.6 Internal Coherence Requirements | Consistency | The demand that domain concepts do not contradict one another. | Centrality, clustering, and connectivity metrics must align; inferred ties must match behavioral flows; relational categories must map onto observed structure; dynamic updates must preserve network logic. |
| | Compatibility | The requirement that entities, variables, and assumptions fit together into a unified descriptive framework. | Requires alignment among tie properties, structural positions, diffusion processes, clustering, brokerage roles, and network evolution models so they co-produce a coherent relational framework. |
| 2. Evidence Layer | 2.1 Observable Phenomena | Observables | The aspects of the domain that can produce detectable signals accessible to measurement. | Tie formation and dissolution; interaction frequency; clustering and subgroup formation; information diffusion; influence cascades; reciprocity patterns; triadic closure; homophily signals; network centralization; bridging and brokerage behavior. |
| | Detection Limits | The boundaries of what can be resolved or sensed by current instruments or methods. | Hidden or unreported ties; offline interactions not captured digitally; algorithmic errors identifying edges; temporal gaps in observation; noisy or ambiguous relational indicators; inability to detect weak/latent ties. |
| 2.2 Measurement Systems | Units | Standardized quantifications (meters, seconds, volts, decibels, dollars, etc.) necessary for consistent comparison. | Degree counts; betweenness/closeness/eigenvector scores; tie weights; frequency intervals; diffusion speed; clustering coefficients; reciprocity rates; network density; structural equivalence indices. |
| | Instruments | Devices and tools (microscopes, spectrometers, sensors, surveys, detectors) used to produce measurements. | Surveys, digital trace data, communication logs, wearable sensors, network-analysis software, ethnographic observation, relational coding schemes, adjacency-matrix extraction tools. |
| 2.3 Operational Definitions | Definitions | Terms defined by specific measurement procedures, ensuring empirical clarity. | Definitions of “tie,” “strength,” “brokerage,” “cluster,” “cohesion,” “structural hole,” “homophily,” “diffusion event,” “reciprocity,” “dyad,” “bridge,” “centrality.” |
| | Procedures | The explicit steps required to perform a measurement in a reproducible way. | Coding relational ties; constructing adjacency matrices; computing centrality and clustering metrics; detecting communities; mapping diffusion chains; identifying brokerage roles; measuring relational similarity; reconstructing temporal networks. |
| 2.4 Data Acquisition | Protocols | Formal processes for gathering data under controlled or standardized conditions. | Survey-based name generators; digital extraction of communication networks; observational logs of interaction episodes; longitudinal relational tracking; organizational network audits; social-media graph sampling. |
| | Sampling | Rules determining which subset of the domain is measured and how representative it is. | Sampling ego networks; selecting dyads/triads; sampling communities or clusters; sampling temporal slices; selecting representative actors across organizations or populations. |
| 2.5 Data Character & Format | Data Types | The form raw evidence takes (time series, spectra, images, counts, qualitative records). | Adjacency lists; weighted graphs; communication-frequency matrices; temporal edge sequences; community-structure mappings; centrality tables; diffusion-cascade logs; coded relational transcripts. |
| | Resolution | The granularity or precision with which data is captured. | Determined by interaction-logging frequency, survey recall accuracy, sensor precision, temporal granularity, network completeness, and tie-strength measurement fidelity. |
| 2.6 Reliability & Calibration | Calibration | Adjustment procedures ensuring instruments produce accurate results. | Verifying network reconstruction accuracy; calibrating sensor thresholds; checking consistency of tie reports; validating community-detection algorithms; cross-checking survey data with digital logs. |
| | Error Characterization | Identification and quantification of noise, uncertainty, bias, and measurement error. | Missing edges; false ties; temporal gaps; sampling bias; cultural misinterpretation of relational cues; centrality-measure instability; structural distortion due to incomplete or noisy data. |
| 3. Structural Layer | 3.1 Patterns & Regularities | Laws / Relations | Stable, repeatable patterns governing how observables behave across conditions. | Triadic closure; preferential attachment; homophily; reciprocity; small-world clustering; diffusion regularities; network centralization; stability of strong/weak tie patterns; assortativity. |
| | Invariants | Quantities or properties that remain constant under transformations (symmetries, conservation laws). | Degree distributions; betweenness and eigenvector centralities; clustering coefficients; subgroup cohesion levels; structural equivalence; stable community membership; persistent brokerage positions. |
| 3.2 Causal Architecture | Mechanisms | Underlying processes or structures that produce the observed regularities. | Tie-formation mechanisms (homophily, proximity, shared context); triadic-closure mechanisms; brokerage and bridging mechanisms; diffusion and contagion mechanisms; structural constraint mechanisms; network evolution dynamics. |
| | Pathways | Organized sequences of interactions forming a causal chain or network. | Information-flow pathways; influence-cascade pathways; bridge-mediated diffusion; pathway of tie strengthening/weakening; subgroup formation sequences; role-shift pathways in evolving networks. |
| 3.3 Theoretical Vocabulary | Concepts | Core terms that encode the domain’s structure (force, gene, equilibrium, field). | Node, tie, dyad, triad, centrality, brokerage, structural hole, homophily, clustering, diffusion, contagion, multiplexity, cohesion, equivalence, assortativity, bridging tie. |
| | Classifications | Taxonomies, categories, or typologies that organize entities and relations. | Core–periphery structures; cohesive clusters; bridging vs bonding ties; directed vs undirected networks; multiplex vs single-layer networks; temporal vs static networks; sparse vs dense networks; assortative vs disassortative patterns. |
| 3.4 Formal Representations | Equations | Mathematical constructs expressing laws, relations, or mechanisms. | Centrality measures (degree, betweenness, eigenvector); clustering formulas; diffusion and contagion equations; triadic-closure probabilities; structural-equivalence metrics; stochastic block model equations; preferential-attachment formulas. |
| | Models | Structured representations—mathematical, computational, or conceptual—used to predict and explain phenomena. | Random graph models (ER); small-world models (Watts–Strogatz); scale-free models (Barabási–Albert); stochastic block models; diffusion models; temporal network models; multiplex relational models. |
| 3.5 Idealized Structures | Simplified Models | Purposeful abstractions that capture essential dynamics while omitting irrelevant detail. | Binary unweighted networks; static snapshots; single-layer networks; homogeneous tie-strength assumptions; symmetric networks; simplified contagion thresholds; idealized triadic-closure environments. |
| | Limit Conditions | Regimes where specific models or approximations hold (classical vs. quantum, linear vs. nonlinear). | Breakdowns in highly dynamic or multiplex networks; hidden ties; biased data; extreme heterogeneity of tie strength; algorithmic detection limits; structural volatility; nonstationary diffusion processes. |
| 3.6 Integrative Frameworks | Unifying Theories | Higher-order structures that connect disparate laws or mechanisms under a coherent whole. | Relational sociology; network-structuralism; social capital theory; diffusion of innovations; collective-action network theory; structural constraint theory; multi-level network integration frameworks. |
| | Interdisciplinary Links | Points where the theory connects to adjacent sciences or larger explanatory systems. | Links to computer science (graph theory, algorithms), epidemiology (contagion models), economics (networked markets), organizational studies (interfirm networks), cognitive science (social cognition), and physics (complex systems). |
| 4. Method Layer | 4.1 Inquiry Design | Experimental Design | Structured plans for manipulating variables to test causal claims. | Manipulating tie opportunities (shared tasks, proximity); altering information flow; adjusting network visibility; introducing potential brokers; modifying boundary conditions to test tie formation, diffusion, and structural shifts. |
| | Observational Design | Systematic approaches for gathering non-manipulated data (surveys, field studies, natural experiments). | Observing natural communication networks; tracking relational evolution; monitoring tie decay/formation; observing diffusion events; identifying emergent clusters, brokers, and boundary patterns without intervention. |
| 4.2 Testing & Validation | Hypothesis Testing | Procedures for evaluating whether evidence supports or contradicts specific claims. | Testing homophily hypotheses; evaluating triadic-closure probabilities; testing influence/diffusion mechanisms; assessing centrality effects; validating community-detection results; testing robustness of network evolution models. |
| | Replication | The requirement that results be independently reproducible under similar conditions. | Reconstructing networks from new datasets; replicating diffusion analyses; repeating centrality computations across time slices; verifying cluster detection in independent samples; rerunning tie-strength estimates. |
| 4.3 Inference & Evaluation | Statistical Inference | Rules for drawing conclusions from noisy or incomplete data. | Estimating tie-formation likelihood; modeling diffusion curves; assessing significance of clustering; evaluating centrality–outcome correlations; computing transition probabilities; comparing network metrics across populations or time. |
| | Model Comparison | Criteria (fit, simplicity, predictive accuracy, robustness) used to evaluate competing models. | Comparing random graph vs small-world vs scale-free fits; contrasting diffusion models; evaluating community-detection algorithms; comparing centrality metrics; testing alternative structural-equivalence frameworks. |
| 4.4 Error Management | Error Analysis | Identification and quantification of random and systematic errors. | Missing-edge errors; false ties from noisy signals; inaccurate temporal ordering; survey recall bias; sampling bias; instability in clustering algorithms; inaccurate weighting of multiplex ties. |
| | Bias Control | Methods for minimizing subjective, instrumental, or procedural biases. | Ensuring representative sampling; cross-validating digital and survey data; using multiple coders; applying threshold tests for tie detection; avoiding overreliance on a single data modality; employing robustness checks across models. |
| 4.5 Adjudication & Revision | Peer Scrutiny | Collective evaluation of claims through critique, review, and debate. | Independent reconstruction of networks; review of coding schemes; reanalysis of diffusion pathways; validation of centrality calculations; critique of boundary-detection assumptions; reassessment of relational interpretations. |
| | Theory Revision | Procedures for modifying, replacing, or discarding models based on new evidence. | Updating tie-formation theories; adjusting diffusion frameworks; revising structural-hole and brokerage models; refining community-formation theories; integrating temporal and multiplex refinements. |
| 4.6 Integrity Conditions | Transparency | Requirements to disclose methods, data, assumptions, and limitations. | Full disclosure of network-construction rules, data sources, tie definitions, weighting criteria, sampling procedures, preprocessing assumptions, and algorithmic parameters. |
| | Ethical Standards | Norms ensuring responsible conduct in experimentation, data handling, and publication. | Protecting confidentiality; preventing reidentification of network actors; avoiding harm in revealing structural positions; responsibly handling sensitive relational data; maintaining neutrality and methodological integrity. |