In analyzing the spatial diffusion of the Swedish Social Democratic Party, this article introduces the notion of a mesolevel network. A mesolevel network is a social network that differs in three important respects from interpersonal microlevel networks directly linking prior and potential adopters of a practice to one another: (1) it is generated by a different causal process than the microlevel network;(2) it tends to be much sparser than the microlevel network; and (3) the typical edge of a mesolevel network bridges much longer sociometric and geographic distances than the typical edge of a microlevel network. These types of mesolevel networks are important because they can dramatically influence the speed at which a contagious practice will diffuse. The mesolevel network focused upon in this article is the network that emerged out of the travel routes of political agitators affiliated with the Social Democratic Party. Computational modeling shows that the diffusion of the Social Democratic Party is likely to have been considerably influenced by the structure of this network. Empirical analyses of the founding of party organizations during the period 1894-1911 support these theoretical predictions and suggest that this mesolevel network was of considerable importance for the diffusion of the Swedish Social Democratic Party.

How does immigrant integration in a country change with immigration density? Guided by a statistical mechanics perspective we propose a novel approach to this problem. The analysis focuses on classical integration quantifiers such as the percentage of jobs (temporary and permanent) given to immigrants, mixed marriages, and newborns with parents of mixed origin. We find that the average values of different quantifiers may exhibit either linear or non-linear growth on immigrant density and we suggest that social action, a concept identified by Max Weber, causes the observed non-linearity. Using the statistical mechanics notion of interaction to quantitatively emulate social action, a unified mathematical model for integration is proposed and it is shown to explain both growth behaviors observed. The linear theory instead, ignoring the possibility of interaction effects would underestimate the quantifiers up to 30% when immigrant densities are low, and overestimate them as much when densities are high. The capacity to quantitatively isolate different types of integration mechanisms makes our framework a suitable tool in the quest for more efficient integration policies.

We apply stochastic process theory to the analysis of immigrant integration. Using a unique and detailed data set from Spain, we study the relationship between local immigrant density and two social and two economic immigration quantifiers for the period 1999-2010. As opposed to the classic time-series approach, by letting immigrant density play the role of 'time' and the quantifier the role of 'space,' it becomes possible to analyse the behavior of the quantifiers by means of continuous time random walks. Two classes of results are then obtained. First, we show that social integration quantifiers evolve following diffusion law, while the evolution of economic quantifiers exhibits ballistic dynamics. Second, we make predictions of best-and worst-case scenarios taking into account large local fluctuations. Our stochastic process approach to integration lends itself to interesting forecasting scenarios which, in the hands of policy makers, have the potential to improve political responses to integration problems. For instance, estimating the standard first-passage time and maximum-span walk reveals local differences in integration performance for different immigration scenarios. Thus, by recognizing the importance of local fluctuations around national means, this research constitutes an important tool to assess the impact of immigration phenomena on municipal budgets and to set up solid multi-ethnic plans at the municipal level as immigration pressures build.

thank Peter Hedström, Fredrik Liljeros, Peter Marsden, the American Sociological Review's editors, anonymous reviewers, and the participants in the Analytical Sociology workgroup at the meeting of the Swedish Sociological Association in Stockholm, 1999, for their valuable comments on earlier versions of this paper. Financial support for the research reported here came from the Swedish Council for Research in the Humanities and the Social Sciences (HSFR) and from the Swedish Foundation for International Cooperation in Research and Higher Education (STINT), and is gratefully acknowledged.

One consistent nding in research on social movement organizations is that new members are recruited along established lines of interaction. Drawing on these ndings, I argue that an individual's decision to leave a social movement organization is the result of similar in uences. Using information about membership turnover over time in a local Swedish temperance organization, I test whether the dropout propensity of existing members is related to prior members' dropout decisions. I nd that existing members' dropout propensity increases when their socially relevant others drop out of the organization. Thus, the results suggest that the decision processes concerning leaving and joining an organization are mirror images. This should have implications for any analysis of social movement organizations because only when this duality of interpersonal in uences is considered can we fully understand the social dynamics of social movement organizations.