Off-campus UMass Amherst users: To download campus access dissertations, please use the following link to log into our proxy server with your UMass Amherst user name and password.

Non-UMass Amherst users: Please talk to your librarian about requesting this dissertation through interlibrary loan.

Dissertations that have an embargo placed on them will not be available to anyone until the embargo expires.

Author ORCID Identifier



Open Access Dissertation

Document Type


Degree Name

Doctor of Philosophy (PhD)

Degree Program

Computer Science

Year Degree Awarded


Month Degree Awarded


First Advisor

Don Towsley

Subject Categories

Computer Sciences


Networks are commonly used to study complex systems. This often requires a good understanding of the structural characteristics and evolution dynamics of networks, and also their impacts on a variety of dynamic processes taking place on top of them. In this thesis, we study various aspects of networks characteristics and dynamics, with a focus on reciprocity, competition and information dissemination. We first formulate the maximum reciprocity problem and study its use in the interpretation of reciprocity in real networks. We propose to interpret reciprocity based on its comparison with the maximum possible reciprocity for a network exhibiting the same degrees. We show that the maximum reciprocity problem is NP-hard, and use an upper bound instead of the maximum. We find that this bound is surprisingly close to the empirical reciprocity in a wide range of real networks, and that there is a surprisingly strong linear relationship between the two. We also show that certain small suboptimal motifs called 3-paths are the major cause for suboptimality in real networks. Secondly, we analyze competition dynamics under cumulative advantage, where accumulated resource promotes gathering even more resource. We characterize the tail distributions of duration and intensity for pairwise competition. We show that duration always has a power-law tail irrespective of competitors' fitness, while intensity has either a power-law tail or an exponential tail depending on whether the competitors are equally fit. We observe a struggle-of-the-fitness phenomenon, where a slight different in fitness results in an extremely heavy tail of duration distribution. Lastly, we study the efficiency of information dissemination in social networks with limited budget of attention. We quantify the efficiency of information dissemination for both cooperative and selfish user behaviors in various network topologies. We identify topologies where cooperation plays a critical role in efficient information propagation. We propose an incentive mechanism called "plus-one" to coax users into cooperation in such cases.