  Exploring the internal mechanism of information spreading is critical for
understanding and controlling the process. Traditional spreading models often
assume individuals play the same role in the spreading process. In reality,
however, individuals' diverse characteristics contribute differently to the
spreading performance, leading to a heterogeneous infection rate across the
system. To investigate network spreading dynamics under heterogeneous infection
rates, we integrate two individual-level features -- influence (i.e., the
ability to influence neighbors) and susceptibility (i.e., the extent to be
influenced by neighbors) -- into the independent cascade model. Our findings
reveal significant differences in spreading performance under heterogeneous and
constant infection rates, with traditional structural centrality metrics
proving more effective in the latter scenario. Additionally, we take the
constant and heterogeneous infection rates into a state-of-the-art maximization
algorithm, the well-known TIM algorithm, and find the seeds selected by
heterogeneous infection rates are more dispersed compared to those under
constant rates. Lastly, we find that both individuals' influence and
susceptibility are vital to the spreading performance. Strikingly, susceptible
individuals are particularly important to spreading when information is
disseminated by social celebrities. By integrating influence and susceptibility
into the spreading model, we gain a more profound understanding of the
underlying mechanisms driving information spreading.
