Methods for calculating influential score by Eqs (6) and (7) are in accordance

Methods for calculating influential score by Eqs (6) and (7) are in accordance with the MTID model. In addition, we define the final score of a user to be the topic-dependent leadership score vector S including scores on all topics, namely, Si ?si terc ??sg terc ?N ??where sg(Iterc) is the vector expression of ground node scores on all of the topics at steady state, and si(Iterc) is the vector expression of user i. The TD-Rank algorithm is summarized in Algorithm 1. Algorithm 1 TD-Rank algorithmInput: Network G = E tweets associated with every user D = 1, . . ., DN V, , D , topic number T; the error threshold to stop the iteration; the maximum iteration times Itermax Output: TD-Rank score of list TDS = [ 1, . . ., SN ] S process D with LDA according to the topic number T for t = 1 TO T do stg ?0 end for connect ground nodes to users with bidirectional edges set the weights on edges for i = 1 TO N do TDS[i] = Si = [ 1/N, . . ., 1/N ] end for while err > or k < Itermax do Temp = TDSk Update every ground node score according to Eq (6) Update every user score according to Eq (7) find the max error: err = Temp - TDSk end whileIn other algorithms akin to Pagerank, the final ranking equation can be defined as: X pj;i PR ?PR ??a ?? ?a?2E??where PR(i) is the rank value of node i, is the decay factor (i.e. the return probability), and pj,i is the transition probability matrix defined by the specific algorithm.PLOS ONE | DOI:10.1371/journal.pone.0158855 July 14,7 /Discover Influential LeadersThere are several drawbacks in applying this algorithm to social networks. First, the return probability is essential. Convergence is guaranteed only on strongly connected networks. In addition, the probability on every edge is identical for all order Dalfopristin users–irrespective of each user’s tweet history. In comparison, our proposed TD-Rank based on the MTID effectively overcomes these shortages. Due to the adoption of ground nodes, TD-Rank extends the advantage of Rocaglamide A manufacturer LeaderRank on every topic view. Moreover, we further adopt a data-driven approach to divide the transition probability into original and retweet probability. Finally, we reconstruct the network into a strongly connected network using the ground nodes and adopt a data-based approach to deal with the transition probability between users and ground nodes, aiming to discover an actual influence score.Results Datasets and experiment settingsTo validate the effectiveness of the TD-Rank algorithm, we test it on crawled data from Weibo, the largest twitter-like social network in China. We start by randomly choosing several active seed users to avoid “Zombie users”–those who have registered but have not posted any tweets. Specifically, we include active users who retweeted more than 20 tweets between May 24th 2013 and May 24th, 2014. With these users, we crawl a network with 211,000 users, 1,612,289 following relationships and 47,002,906 total tweets. The detaileded statistics for this dataset are are listed in Table 1. The only parameters that must be set are the LDA parameters, which reflect the number of ground nodes selected. The LDA is tuned by three parameters: the Dirichlet hyper-parameters , and topic number T. In this paper, these parameters are set as T = 20, = 50/T + 1, and = 0.1 + 1 in Spark [23]. Obviously, choosing different values for these parameters has implications for the model results. However, this is a basically a model selection problem, which is not the focus of this paper.Methods for calculating influential score by Eqs (6) and (7) are in accordance with the MTID model. In addition, we define the final score of a user to be the topic-dependent leadership score vector S including scores on all topics, namely, Si ?si terc ??sg terc ?N ??where sg(Iterc) is the vector expression of ground node scores on all of the topics at steady state, and si(Iterc) is the vector expression of user i. The TD-Rank algorithm is summarized in Algorithm 1. Algorithm 1 TD-Rank algorithmInput: Network G = E tweets associated with every user D = 1, . . ., DN V, , D , topic number T; the error threshold to stop the iteration; the maximum iteration times Itermax Output: TD-Rank score of list TDS = [ 1, . . ., SN ] S process D with LDA according to the topic number T for t = 1 TO T do stg ?0 end for connect ground nodes to users with bidirectional edges set the weights on edges for i = 1 TO N do TDS[i] = Si = [ 1/N, . . ., 1/N ] end for while err > or k < Itermax do Temp = TDSk Update every ground node score according to Eq (6) Update every user score according to Eq (7) find the max error: err = Temp - TDSk end whileIn other algorithms akin to Pagerank, the final ranking equation can be defined as: X pj;i PR ?PR ??a ?? ?a?2E??where PR(i) is the rank value of node i, is the decay factor (i.e. the return probability), and pj,i is the transition probability matrix defined by the specific algorithm.PLOS ONE | DOI:10.1371/journal.pone.0158855 July 14,7 /Discover Influential LeadersThere are several drawbacks in applying this algorithm to social networks. First, the return probability is essential. Convergence is guaranteed only on strongly connected networks. In addition, the probability on every edge is identical for all users–irrespective of each user’s tweet history. In comparison, our proposed TD-Rank based on the MTID effectively overcomes these shortages. Due to the adoption of ground nodes, TD-Rank extends the advantage of LeaderRank on every topic view. Moreover, we further adopt a data-driven approach to divide the transition probability into original and retweet probability. Finally, we reconstruct the network into a strongly connected network using the ground nodes and adopt a data-based approach to deal with the transition probability between users and ground nodes, aiming to discover an actual influence score.Results Datasets and experiment settingsTo validate the effectiveness of the TD-Rank algorithm, we test it on crawled data from Weibo, the largest twitter-like social network in China. We start by randomly choosing several active seed users to avoid “Zombie users”–those who have registered but have not posted any tweets. Specifically, we include active users who retweeted more than 20 tweets between May 24th 2013 and May 24th, 2014. With these users, we crawl a network with 211,000 users, 1,612,289 following relationships and 47,002,906 total tweets. The detaileded statistics for this dataset are are listed in Table 1. The only parameters that must be set are the LDA parameters, which reflect the number of ground nodes selected. The LDA is tuned by three parameters: the Dirichlet hyper-parameters , and topic number T. In this paper, these parameters are set as T = 20, = 50/T + 1, and = 0.1 + 1 in Spark [23]. Obviously, choosing different values for these parameters has implications for the model results. However, this is a basically a model selection problem, which is not the focus of this paper.