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With the continuous enrichment of social network applications, such as TikTok, Weibo, Twitter, and others, social media have become an indispensable part of our lives. Web users can participate in their favorite events or pay attention to people they like. The “heterogeneous” influence between events and users can be effectively modeled, and users’ potential future behaviors can be predicted, so as to facilitate applications such as recommendations and online advertising. For example, a user’s favorite live streaming host (user) recommends certain products (event), can we predict whether the user will buy these products in the future? The majority of studies are based on a homogeneous graph neural network to model the influence between users. However, these studies ignore the impact of events on users in reality. For instance, when users purchase commodities through live streaming channels, in addition to the factors of the host, the commodity is also a key factor that influences the behavior of users. This study designs an influence prediction model based on a heterogeneous neural network HetInf. Specifically, we first constructed the heterogeneous social influence network according to the relationship between event nodes and user nodes, then sampled the user heterogeneous subgraph for each user, extracted the relevant node features, and finally predicted the probability of user behavior through the heterogeneous neural network model. We conducted comprehensive experiments on two large social network datasets. Furthermore, the experimental results show that HetInf is significantly superior to the previous homogeneous neural network methods.

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To determine the effects of Ti and mixing entropy (ΔSmix) on the structure and mechanical proper-ties of Zr-Ta alloys and then find a new potential energetic structural material with good me-chanical properties and more reactive elements, TixZr2.5−xTa (x = 0, 0.5, 1.0, 1.5, 2.0) alloys were investigated. The XRD experimental results showed that the phase transformation of TixZr2.5−xTa nonequal-ratio ternary alloys depended not on the value of ΔSmix, but on the amount of Ti atoms. With the addition of Ti, the content of the HCP phase decreased gradually. SEM analyses revealed that dendrite morphology and component segregation increasingly developed and then weakened gradually. When x increases to 2.0, TixZr2.5−xTa with the best mechanical properties can be ob-tained. The yield strength, compressive strength and fracture strain of Ti2.0Zr0.5Ta reached 883 MPa, 1568 MPa and 34.58%, respectively. The dependence of the phase transformation and me-chanical properties confirms that improving the properties of Zr-Ta alloys by doping Ti is feasible.