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Figure 2 The Procedure of the proposed RLink of Reinforcement Learning based User Identity Linkage. The blue link represents friend relation in social network, and orange line at state S; represents matched identity pair. At time 7, agent generates a pair of matching identities as an action according to current state. After agent performs this action, the state would be changed at time i + | and next action can be generated based on Sj+1

Figure 2 The Procedure of the proposed RLink of Reinforcement Learning based User Identity Linkage. The blue link represents friend relation in social network, and orange line at state S; represents matched identity pair. At time 7, agent generates a pair of matching identities as an action according to current state. After agent performs this action, the state would be changed at time i + | and next action can be generated based on Sj+1

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adjusted the linkage strategy after each matching step due to the high complexity. In order to model this long-term influence effectively, we novelly consider UIL as a Markov Decision Process and propose a deep reinforcement learning framework (RLink) to automatically match identities in two different social networks. Figure 2 illustrates the overall RLink process. One state consists of three components, i.e. two social net- work structures and previously matched identity pairs. According to the current state, the agent performs an action. After the action is performed, the state would be changed at the next time and a reward would be fed to the agent to adjust its policy. Because the action space is large and dynamic in the UIL process, we adapt an Actor-Critic frame- work [32], in which the actor network generates a deterministic action based on current state and the critic network evaluates the quality of this action-state pair. Concretely, for
adjusted the linkage strategy after each matching step due to the high complexity. In order to model this long-term influence effectively, we novelly consider UIL as a Markov Decision Process and propose a deep reinforcement learning framework (RLink) to automatically match identities in two different social networks. Figure 2 illustrates the overall RLink process. One state consists of three components, i.e. two social net- work structures and previously matched identity pairs. According to the current state, the agent performs an action. After the action is performed, the state would be changed at the next time and a reward would be fed to the agent to adjust its policy. Because the action space is large and dynamic in the UIL process, we adapt an Actor-Critic frame- work [32], in which the actor network generates a deterministic action based on current state and the critic network evaluates the quality of this action-state pair. Concretely, for
Figure 3 The framework of Actor-Critic network, where the actor is comprised of an Encoder-Decoder architecture and the critic is DQN. The inputs of Actor are history identity pairs and network structure, where history identity pairs were generated by our Actor-Critic network before current epoch (See (4)). Remarkably, ho is a zero vector. Then this action and current state are input onto the Critic to evaluate the quality of this action
Figure 3 The framework of Actor-Critic network, where the actor is comprised of an Encoder-Decoder architecture and the critic is DQN. The inputs of Actor are history identity pairs and network structure, where history identity pairs were generated by our Actor-Critic network before current epoch (See (4)). Remarkably, ho is a zero vector. Then this action and current state are input onto the Critic to evaluate the quality of this action
Table 3 Performance comparison on user identity linkage
Table 3 Performance comparison on user identity linkage
Figure 4 Detailed Performance Comparison on Twitter-Foursquare Dataset
Figure 4 Detailed Performance Comparison on Twitter-Foursquare Dataset
Figure 5 Comparison between DDPG and DQN: The x-axis shows the training episodes. The y-axis shows the total reward of each episode. Red line represents DDPG and green Line is DON
Figure 5 Comparison between DDPG and DQN: The x-axis shows the training episodes. The y-axis shows the total reward of each episode. Red line represents DDPG and green Line is DON
Figure6 Q-value performance with different reward on Last.fm-Myspace and Linkedin-Arminer. The x-axis shows the training episodes. The y-axis shows the normalize Q-value of each session. The value of immediate reward is equal to 1/ — 1, while long-term reward is } / el
Figure6 Q-value performance with different reward on Last.fm-Myspace and Linkedin-Arminer. The x-axis shows the training episodes. The y-axis shows the normalize Q-value of each session. The value of immediate reward is equal to 1/ — 1, while long-term reward is } / el
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