Social networks are everywhere, from face-to-face activities to online social networks such as Flickr, YouTube and Facebook. In social networks, ties (relationships) are connections between people. The change of social relationships over time consequently leads to the evolution of the social network structure. At the same time, ties serve as carriers to transfer pieces of information from one person to another. Studying social ties is critical to understanding the fundamental processes behind the network. Although many studies on social networks have been carried out over the last many decades, most of the work either used small in-lab datasets, or focused on directly connected static relations while ignoring indirect relations and the dynamic nature of real networks. Today, because of the emergence of online social networks, more and more large longitudinal social datasets are becoming available. The available real social datasets are fundamental to understanding evolution processes of networks in more depth. In this thesis, we study the role of social ties in dynamic networks using datasets from various domains of online social networks. Networks, especially social networks often exhibit dual dynamic nature: the structure of the graph changes (by node and edge insertion and removal), and information flows in the network. Our work focuses on both aspects of network dynamics. The purpose of this work is to better understand the role of social ties in network evolution and changes over time, and to determine what social factors help shape individuals’ choices in negative behavior. We first developed a metric that measures the strength of indirectly connected ties. We validated the accuracy of the measurement of indirect tie metric with real-world social datasets from four domains. Another important aspect of my research is the study of edge creation and forecast future graph structure in time evolving networks. We aim to develop algorithms that explain the edge formation properties and process which govern the network evolution. We also designed algorithms in the information propagation process to identify next spreaders several steps ahead, and use them to predict diffusion paths. Next, because different social ties or social ties in different contexts have different influence between people, we looked at the influence of social ties in behavior contagion, particularly in a negative behavior cheating. Our recent work included the study of social factors that motivate or limit the contagion of cheating in a large real-world online social network. We tested several factors drawn from sociology and psychology explaining cheating behavior but have remained untested outside of controlled laboratory experiments or only with small, survey based studies. In addition, this work analyzed online social networks with large datasets that certain inherent influences or patterns only emerge or become visible when dealing with massive data. We analyzed the world’s largest online gaming community, Steam Community, collected data with 3, 148, 289 users and 44, 725, 277 edges. We also made interesting observations of cheating influence that were not observed in previous in-lab experiments. Besides providing empirically based understanding of social ties and their influence in evolving networks at large scales, our work has high practical importance for using social influence to maintain a fair online community environment, and build systems to detect, prevent, and mitigate undesirable influence.