Table 2.

Summary of the typology extraction methods considered in the article.

	Subgroup-based method (T1)	BDG method	Modified BDG method (T2)
I. Description of ego-networks
	1. Apply GN community detection algorithm to each ego-network. ►Obtained partition of each ego-network’s nodes into cohesive subgroups.	1. Qualitatively identify main types in ego-network visualizations. ►6 types identified: regular dense, centered dense, centered start, segmented, pearl collar, dispersed.	1. Calculate 6 BDG measures on each ego-network: network density, betweenness centralization, modularity of Louvain subgroup partition, number of components, relative size of the main component, network diameter.
	2. Calculate 3 summary measures on GN partition of each ego-network: count of subgroups with 3 or more nodes; count of subgroups with 1 or 2 nodes; modularity of partition.	2. Qualitatively select one representative ego-network for each of the 6 types.
		3. Identify structural measures that best discriminate between types. ►6 measures identified: network density, betweenness centralization, modularity of Louvain subgroup partition, number of components, relative size of the main component, network diameter.
		4. Calculate 6 measures from Step 3 on each ego-network.
II. Extraction of typology
	3. Conduct k-medoid cluster analysis on the 3 summary measures obtained in Step 2, with k ranging from 2 to 20.	5. Conduct discriminant analysis based on ego-networks selected in Step 2 and measures calculated in step 4. ►6 assignment probabilities (one for each type) obtained for each ego-network in the data.	2. Conduct k-medoid cluster analysis on the 6 BDG measures obtained in Step 1, with k ranging from 2 to 20.
	4. Select optimal clustering partition (typology) by selecting k* based on AIC (inflection point) and silhouette (local maximum).		3. Select optimal clustering partition (typology) by selecting k* based on AIC (inflection point) and silhouette (local maximum).
III. Assignment of ego-networks to types
	5. In optimal clustering partition, each ego-network is assigned to a type (i.e., a k-medoid cluster).	6. Assign ego-network to type for which it has highest assignment probability. Ego-networks with equal probability on multiple types are assigned qualitatively or not assigned to any type.	4. In optimal clustering partition, each ego-network is assigned to a type (i.e., a k-medoid cluster).