As an important carrier of information diffusion, social media has experienced a huge increase in the number of users and also has a big effect on the way of how information diffuses. For example, Facebook and Youtube have attracted more than 1.6 and 1.3 billion users until 2020, respectively. The use of internet and online social network have largely reduced the cost of information propagation and sharing. Besides users and content-based features, social network properties are critical factors that may affect information diffusion. In this thesis, we focus on the influence of temporal network properties on information spreading. As researchers have proved that similar users tend to spread similar content of information, we further propose how to design network representation learning algorithms to better capture node similarity in a network. The first part of the thesis is mainly about how the local properties of nodes and links would affect information spreading on temporal networks. Chapter 2 studies which links are likely to appear in an information diffusion trajectory. We simulate the information diffusion process by a susceptible-infected (SI) model on various empirical temporal networks. An information diffusion backbone is proposed to characterize the probability of a link to appear in the diffusion trajectory. Due to the high complexity of constructing diffusion backbone, we further propose time-scaled weight to identify which links would appear in the diffusion backbone. Compared to the centrality metrics derived from static networks, time-scaled weight shows better identification performance. The conclusions in this chapter may inspire how to maximize information diffusion on temporal networks by deliberately choosing links to transmit information. Chapter 3 investigates which links should be temporally blocked in order to suppress information diffusion on temporal networks. We rank the links by different blocking strategies based on the link properties on static and temporal networks, including the ones derived from information diffusion backbone. We remove the links with high ranking values based on blocking strategies for a given time period. We show that four link blocking strategies outperform the others in suppressing information diffusion. The results show that the effectiveness of the metrics on suppressing information diffusion largely depends on the network properties. In chapter 4, we study how to identify influential nodes, i.e., nodes serving as the seed can spread information widely, on temporal networks. The information diffusion process is simulated by susceptible-infected-recovered (SIR) model on various empirical temporal networks. We propose a temporal information gathering process (Tig-process), which can iteratively gather neighboring information though temporal path, to identify influential nodes. Compared to the benchmark metrics, Tig-process can better identify influential nodes across different temporal networks with a small cost. The experimental designs and results in these three chapters further inspire us to study the local surrounding properties of nodes and links for other spreading processes as well as other types of networks. In the second part of the thesis, we work on designing network embedding algorithms to embed nodes to a low-dimensional space, which can make similar nodes be close in the embedding space. Chapter 5 designs a degree-biased random walk, i.e., DiaRW, to sample walks from a static network. If the source node of a random walk has higher degree, the walk length tends to be longer. Also, if a random walker walks to a low-degree node, the probability of backtracking the former high-degree node is higher. The node pairs generated from walks are further used as input for a learning model, i.e., Skip-Gram model. We unveil that DiaRW shows better performance compared to baseline embedding algorithms on tasks, e.g., link prediction and node classification. Chapter 6 proposes SI-spreading-based network embedding algorithms. We apply SI model on static and temporal networks to sample trajectories. The node pairs generated from trajectories are also used as input for Skip-Gram model. We show SI-spreading-based network embedding algorithms perform better than random-walk-based network embedding algorithms on missing link prediction task. Both of the two chapters consider node heterogeneity in designing embedding algorithms. The last chapter proposes insight of the thesis based on the research questions and provides the possible future directions that is related to our research.